Add files using upload-large-folder tool

llava_next/include/python3.10/bltinmodule.h

@@ -0,0 +1,14 @@
+#ifndef Py_BLTINMODULE_H
+#define Py_BLTINMODULE_H
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+PyAPI_DATA(PyTypeObject) PyFilter_Type;
+PyAPI_DATA(PyTypeObject) PyMap_Type;
+PyAPI_DATA(PyTypeObject) PyZip_Type;
+
+#ifdef __cplusplus
+}
+#endif
+#endif /* !Py_BLTINMODULE_H */

llava_next/include/python3.10/fileobject.h

@@ -0,0 +1,49 @@
+/* File object interface (what's left of it -- see io.py) */
+
+#ifndef Py_FILEOBJECT_H
+#define Py_FILEOBJECT_H
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#define PY_STDIOTEXTMODE "b"
+
+PyAPI_FUNC(PyObject *) PyFile_FromFd(int, const char *, const char *, int,
+                                     const char *, const char *,
+                                     const char *, int);
+PyAPI_FUNC(PyObject *) PyFile_GetLine(PyObject *, int);
+PyAPI_FUNC(int) PyFile_WriteObject(PyObject *, PyObject *, int);
+PyAPI_FUNC(int) PyFile_WriteString(const char *, PyObject *);
+PyAPI_FUNC(int) PyObject_AsFileDescriptor(PyObject *);
+
+/* The default encoding used by the platform file system APIs
+   If non-NULL, this is different than the default encoding for strings
+*/
+PyAPI_DATA(const char *) Py_FileSystemDefaultEncoding;
+#if !defined(Py_LIMITED_API) || Py_LIMITED_API+0 >= 0x03060000
+PyAPI_DATA(const char *) Py_FileSystemDefaultEncodeErrors;
+#endif
+PyAPI_DATA(int) Py_HasFileSystemDefaultEncoding;
+
+#if !defined(Py_LIMITED_API) || Py_LIMITED_API+0 >= 0x03070000
+PyAPI_DATA(int) Py_UTF8Mode;
+#endif
+
+/* A routine to check if a file descriptor can be select()-ed. */
+#ifdef _MSC_VER
+    /* On Windows, any socket fd can be select()-ed, no matter how high */
+    #define _PyIsSelectable_fd(FD) (1)
+#else
+    #define _PyIsSelectable_fd(FD) ((unsigned int)(FD) < (unsigned int)FD_SETSIZE)
+#endif
+
+#ifndef Py_LIMITED_API
+#  define Py_CPYTHON_FILEOBJECT_H
+#  include  "cpython/fileobject.h"
+#  undef Py_CPYTHON_FILEOBJECT_H
+#endif
+
+#ifdef __cplusplus
+}
+#endif
+#endif /* !Py_FILEOBJECT_H */

llava_next/include/python3.10/opcode.h

@@ -0,0 +1,172 @@
+/* Auto-generated by Tools/scripts/generate_opcode_h.py from Lib/opcode.py */
+#ifndef Py_OPCODE_H
+#define Py_OPCODE_H
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+
+    /* Instruction opcodes for compiled code */
+#define POP_TOP                   1
+#define ROT_TWO                   2
+#define ROT_THREE                 3
+#define DUP_TOP                   4
+#define DUP_TOP_TWO               5
+#define ROT_FOUR                  6
+#define NOP                       9
+#define UNARY_POSITIVE           10
+#define UNARY_NEGATIVE           11
+#define UNARY_NOT                12
+#define UNARY_INVERT             15
+#define BINARY_MATRIX_MULTIPLY   16
+#define INPLACE_MATRIX_MULTIPLY  17
+#define BINARY_POWER             19
+#define BINARY_MULTIPLY          20
+#define BINARY_MODULO            22
+#define BINARY_ADD               23
+#define BINARY_SUBTRACT          24
+#define BINARY_SUBSCR            25
+#define BINARY_FLOOR_DIVIDE      26
+#define BINARY_TRUE_DIVIDE       27
+#define INPLACE_FLOOR_DIVIDE     28
+#define INPLACE_TRUE_DIVIDE      29
+#define GET_LEN                  30
+#define MATCH_MAPPING            31
+#define MATCH_SEQUENCE           32
+#define MATCH_KEYS               33
+#define COPY_DICT_WITHOUT_KEYS   34
+#define WITH_EXCEPT_START        49
+#define GET_AITER                50
+#define GET_ANEXT                51
+#define BEFORE_ASYNC_WITH        52
+#define END_ASYNC_FOR            54
+#define INPLACE_ADD              55
+#define INPLACE_SUBTRACT         56
+#define INPLACE_MULTIPLY         57
+#define INPLACE_MODULO           59
+#define STORE_SUBSCR             60
+#define DELETE_SUBSCR            61
+#define BINARY_LSHIFT            62
+#define BINARY_RSHIFT            63
+#define BINARY_AND               64
+#define BINARY_XOR               65
+#define BINARY_OR                66
+#define INPLACE_POWER            67
+#define GET_ITER                 68
+#define GET_YIELD_FROM_ITER      69
+#define PRINT_EXPR               70
+#define LOAD_BUILD_CLASS         71
+#define YIELD_FROM               72
+#define GET_AWAITABLE            73
+#define LOAD_ASSERTION_ERROR     74
+#define INPLACE_LSHIFT           75
+#define INPLACE_RSHIFT           76
+#define INPLACE_AND              77
+#define INPLACE_XOR              78
+#define INPLACE_OR               79
+#define LIST_TO_TUPLE            82
+#define RETURN_VALUE             83
+#define IMPORT_STAR              84
+#define SETUP_ANNOTATIONS        85
+#define YIELD_VALUE              86
+#define POP_BLOCK                87
+#define POP_EXCEPT               89
+#define HAVE_ARGUMENT            90
+#define STORE_NAME               90
+#define DELETE_NAME              91
+#define UNPACK_SEQUENCE          92
+#define FOR_ITER                 93
+#define UNPACK_EX                94
+#define STORE_ATTR               95
+#define DELETE_ATTR              96
+#define STORE_GLOBAL             97
+#define DELETE_GLOBAL            98
+#define ROT_N                    99
+#define LOAD_CONST              100
+#define LOAD_NAME               101
+#define BUILD_TUPLE             102
+#define BUILD_LIST              103
+#define BUILD_SET               104
+#define BUILD_MAP               105
+#define LOAD_ATTR               106
+#define COMPARE_OP              107
+#define IMPORT_NAME             108
+#define IMPORT_FROM             109
+#define JUMP_FORWARD            110
+#define JUMP_IF_FALSE_OR_POP    111
+#define JUMP_IF_TRUE_OR_POP     112
+#define JUMP_ABSOLUTE           113
+#define POP_JUMP_IF_FALSE       114
+#define POP_JUMP_IF_TRUE        115
+#define LOAD_GLOBAL             116
+#define IS_OP                   117
+#define CONTAINS_OP             118
+#define RERAISE                 119
+#define JUMP_IF_NOT_EXC_MATCH   121
+#define SETUP_FINALLY           122
+#define LOAD_FAST               124
+#define STORE_FAST              125
+#define DELETE_FAST             126
+#define GEN_START               129
+#define RAISE_VARARGS           130
+#define CALL_FUNCTION           131
+#define MAKE_FUNCTION           132
+#define BUILD_SLICE             133
+#define LOAD_CLOSURE            135
+#define LOAD_DEREF              136
+#define STORE_DEREF             137
+#define DELETE_DEREF            138
+#define CALL_FUNCTION_KW        141
+#define CALL_FUNCTION_EX        142
+#define SETUP_WITH              143
+#define EXTENDED_ARG            144
+#define LIST_APPEND             145
+#define SET_ADD                 146
+#define MAP_ADD                 147
+#define LOAD_CLASSDEREF         148
+#define MATCH_CLASS             152
+#define SETUP_ASYNC_WITH        154
+#define FORMAT_VALUE            155
+#define BUILD_CONST_KEY_MAP     156
+#define BUILD_STRING            157
+#define LOAD_METHOD             160
+#define CALL_METHOD             161
+#define LIST_EXTEND             162
+#define SET_UPDATE              163
+#define DICT_MERGE              164
+#define DICT_UPDATE             165
+#ifdef NEED_OPCODE_JUMP_TABLES
+static uint32_t _PyOpcode_RelativeJump[8] = {
+    0U,
+    0U,
+    536870912U,
+    67125248U,
+    67141632U,
+    0U,
+    0U,
+    0U,
+};
+static uint32_t _PyOpcode_Jump[8] = {
+    0U,
+    0U,
+    536870912U,
+    101695488U,
+    67141632U,
+    0U,
+    0U,
+    0U,
+};
+#endif /* OPCODE_TABLES */
+
+/* EXCEPT_HANDLER is a special, implicit block type which is created when
+   entering an except handler. It is not an opcode but we define it here
+   as we want it to be available to both frameobject.c and ceval.c, while
+   remaining private.*/
+#define EXCEPT_HANDLER 257
+
+#define HAS_ARG(op) ((op) >= HAVE_ARGUMENT)
+
+#ifdef __cplusplus
+}
+#endif
+#endif /* !Py_OPCODE_H */

llava_next/include/python3.10/patchlevel.h

@@ -0,0 +1,35 @@
+
+/* Python version identification scheme.
+
+   When the major or minor version changes, the VERSION variable in
+   configure.ac must also be changed.
+
+   There is also (independent) API version information in modsupport.h.
+*/
+
+/* Values for PY_RELEASE_LEVEL */
+#define PY_RELEASE_LEVEL_ALPHA  0xA
+#define PY_RELEASE_LEVEL_BETA   0xB
+#define PY_RELEASE_LEVEL_GAMMA  0xC     /* For release candidates */
+#define PY_RELEASE_LEVEL_FINAL  0xF     /* Serial should be 0 here */
+                                        /* Higher for patch releases */
+
+/* Version parsed out into numeric values */
+/*--start constants--*/
+#define PY_MAJOR_VERSION        3
+#define PY_MINOR_VERSION        10
+#define PY_MICRO_VERSION        16
+#define PY_RELEASE_LEVEL        PY_RELEASE_LEVEL_FINAL
+#define PY_RELEASE_SERIAL       0
+
+/* Version as a string */
+#define PY_VERSION              "3.10.16"
+/*--end constants--*/
+
+/* Version as a single 4-byte hex number, e.g. 0x010502B2 == 1.5.2b2.
+   Use this for numeric comparisons, e.g. #if PY_VERSION_HEX >= ... */
+#define PY_VERSION_HEX ((PY_MAJOR_VERSION << 24) | \
+                        (PY_MINOR_VERSION << 16) | \
+                        (PY_MICRO_VERSION <<  8) | \
+                        (PY_RELEASE_LEVEL <<  4) | \
+                        (PY_RELEASE_SERIAL << 0))

llava_next/include/python3.10/pylifecycle.h

@@ -0,0 +1,74 @@
+
+/* Interfaces to configure, query, create & destroy the Python runtime */
+
+#ifndef Py_PYLIFECYCLE_H
+#define Py_PYLIFECYCLE_H
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+
+/* Initialization and finalization */
+PyAPI_FUNC(void) Py_Initialize(void);
+PyAPI_FUNC(void) Py_InitializeEx(int);
+PyAPI_FUNC(void) Py_Finalize(void);
+#if !defined(Py_LIMITED_API) || Py_LIMITED_API+0 >= 0x03060000
+PyAPI_FUNC(int) Py_FinalizeEx(void);
+#endif
+PyAPI_FUNC(int) Py_IsInitialized(void);
+
+/* Subinterpreter support */
+PyAPI_FUNC(PyThreadState *) Py_NewInterpreter(void);
+PyAPI_FUNC(void) Py_EndInterpreter(PyThreadState *);
+
+
+/* Py_PyAtExit is for the atexit module, Py_AtExit is for low-level
+ * exit functions.
+ */
+PyAPI_FUNC(int) Py_AtExit(void (*func)(void));
+
+PyAPI_FUNC(void) _Py_NO_RETURN Py_Exit(int);
+
+/* Bootstrap __main__ (defined in Modules/main.c) */
+PyAPI_FUNC(int) Py_Main(int argc, wchar_t **argv);
+PyAPI_FUNC(int) Py_BytesMain(int argc, char **argv);
+
+/* In pathconfig.c */
+PyAPI_FUNC(void) Py_SetProgramName(const wchar_t *);
+PyAPI_FUNC(wchar_t *) Py_GetProgramName(void);
+
+PyAPI_FUNC(void) Py_SetPythonHome(const wchar_t *);
+PyAPI_FUNC(wchar_t *) Py_GetPythonHome(void);
+
+PyAPI_FUNC(wchar_t *) Py_GetProgramFullPath(void);
+
+PyAPI_FUNC(wchar_t *) Py_GetPrefix(void);
+PyAPI_FUNC(wchar_t *) Py_GetExecPrefix(void);
+PyAPI_FUNC(wchar_t *) Py_GetPath(void);
+PyAPI_FUNC(void)      Py_SetPath(const wchar_t *);
+#ifdef MS_WINDOWS
+int _Py_CheckPython3(void);
+#endif
+
+/* In their own files */
+PyAPI_FUNC(const char *) Py_GetVersion(void);
+PyAPI_FUNC(const char *) Py_GetPlatform(void);
+PyAPI_FUNC(const char *) Py_GetCopyright(void);
+PyAPI_FUNC(const char *) Py_GetCompiler(void);
+PyAPI_FUNC(const char *) Py_GetBuildInfo(void);
+
+/* Signals */
+typedef void (*PyOS_sighandler_t)(int);
+PyAPI_FUNC(PyOS_sighandler_t) PyOS_getsig(int);
+PyAPI_FUNC(PyOS_sighandler_t) PyOS_setsig(int, PyOS_sighandler_t);
+
+#ifndef Py_LIMITED_API
+#  define Py_CPYTHON_PYLIFECYCLE_H
+#  include  "cpython/pylifecycle.h"
+#  undef Py_CPYTHON_PYLIFECYCLE_H
+#endif
+
+#ifdef __cplusplus
+}
+#endif
+#endif /* !Py_PYLIFECYCLE_H */

llava_next/include/python3.10/structmember.h

@@ -0,0 +1,75 @@
+#ifndef Py_STRUCTMEMBER_H
+#define Py_STRUCTMEMBER_H
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+
+/* Interface to map C struct members to Python object attributes */
+
+#include <stddef.h> /* For offsetof */
+
+/* An array of PyMemberDef structures defines the name, type and offset
+   of selected members of a C structure.  These can be read by
+   PyMember_GetOne() and set by PyMember_SetOne() (except if their READONLY
+   flag is set).  The array must be terminated with an entry whose name
+   pointer is NULL. */
+
+typedef struct PyMemberDef {
+    const char *name;
+    int type;
+    Py_ssize_t offset;
+    int flags;
+    const char *doc;
+} PyMemberDef;
+
+/* Types */
+#define T_SHORT     0
+#define T_INT       1
+#define T_LONG      2
+#define T_FLOAT     3
+#define T_DOUBLE    4
+#define T_STRING    5
+#define T_OBJECT    6
+/* XXX the ordering here is weird for binary compatibility */
+#define T_CHAR      7   /* 1-character string */
+#define T_BYTE      8   /* 8-bit signed int */
+/* unsigned variants: */
+#define T_UBYTE     9
+#define T_USHORT    10
+#define T_UINT      11
+#define T_ULONG     12
+
+/* Added by Jack: strings contained in the structure */
+#define T_STRING_INPLACE    13
+
+/* Added by Lillo: bools contained in the structure (assumed char) */
+#define T_BOOL      14
+
+#define T_OBJECT_EX 16  /* Like T_OBJECT, but raises AttributeError
+                           when the value is NULL, instead of
+                           converting to None. */
+#define T_LONGLONG      17
+#define T_ULONGLONG     18
+
+#define T_PYSSIZET      19      /* Py_ssize_t */
+#define T_NONE          20      /* Value is always None */
+
+
+/* Flags */
+#define READONLY            1
+#define READ_RESTRICTED     2
+#define PY_WRITE_RESTRICTED 4
+#define RESTRICTED          (READ_RESTRICTED | PY_WRITE_RESTRICTED)
+
+#define PY_AUDIT_READ       READ_RESTRICTED
+
+/* Current API, use this */
+PyAPI_FUNC(PyObject *) PyMember_GetOne(const char *, struct PyMemberDef *);
+PyAPI_FUNC(int) PyMember_SetOne(char *, struct PyMemberDef *, PyObject *);
+
+
+#ifdef __cplusplus
+}
+#endif
+#endif /* !Py_STRUCTMEMBER_H */

parrot/lib/python3.10/site-packages/torch/include/c10/core/AutogradState.h

@@ -0,0 +1,72 @@
+#pragma once
+
+#include <c10/macros/Export.h>
+
+namespace c10 {
+
+// Structure used to pack all the thread local boolean
+// flags used by autograd
+struct C10_API AutogradState {
+  static AutogradState& get_tls_state();
+  static void set_tls_state(AutogradState state);
+
+  AutogradState(
+      bool grad_mode,
+      bool inference_mode,
+      bool fw_grad_mode,
+      bool multithreading_enabled)
+      : grad_mode_(grad_mode),
+        inference_mode_(inference_mode),
+        fw_grad_mode_(fw_grad_mode),
+        multithreading_enabled_(multithreading_enabled),
+        view_replay_enabled_(false) {}
+
+  void set_grad_mode(bool enabled) {
+    grad_mode_ = enabled;
+  }
+
+  void set_fw_grad_mode(bool enabled) {
+    fw_grad_mode_ = enabled;
+  }
+
+  void set_inference_mode(bool enabled) {
+    inference_mode_ = enabled;
+  }
+
+  void set_multithreading_enabled(bool multithreading_enabled) {
+    multithreading_enabled_ = multithreading_enabled;
+  }
+
+  void set_view_replay_enabled(bool view_replay_enabled) {
+    view_replay_enabled_ = view_replay_enabled;
+  }
+
+  bool get_grad_mode() const {
+    return grad_mode_;
+  }
+
+  bool get_fw_grad_mode() const {
+    return fw_grad_mode_;
+  }
+
+  bool get_inference_mode() const {
+    return inference_mode_;
+  }
+
+  bool get_multithreading_enabled() const {
+    return multithreading_enabled_;
+  }
+
+  bool get_view_replay_enabled() const {
+    return view_replay_enabled_;
+  }
+
+ private:
+  bool grad_mode_ : 1;
+  bool inference_mode_ : 1;
+  bool fw_grad_mode_ : 1;
+  bool multithreading_enabled_ : 1;
+  bool view_replay_enabled_ : 1;
+};
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/Backend.h

@@ -0,0 +1,387 @@
+#pragma once
+
+#include <c10/core/DeviceType.h>
+#include <c10/core/DispatchKey.h>
+#include <c10/core/DispatchKeySet.h>
+#include <c10/util/Exception.h>
+
+#include <stdexcept>
+
+namespace c10 {
+
+/**
+ * This legacy enum class defines the set of backends supported by old school,
+ * code generated Type-based ATen.  A "backend" in this sense roughly
+ * corresponds to the cartesian product of (device type, layout), but restricted
+ * only to combinations which we actually have kernels for.  Backend does NOT
+ * include dtype.
+ *
+ * The reason we are sunsetting this enum class is because it doesn't allow for
+ * open registration; e.g., if you want to add SparseXLA, you'd have to
+ * edit this enum; you wouldn't be able to do it out of tree.  DispatchKey is
+ * the replacement for Backend which supports open registration.
+ *
+ * NB: The concept of 'Backend' here disagrees with the notion of backend
+ * exposed to users in torch.backends.  Backend here is something like "CPU"
+ * or "SparseCUDA"; backend in torch.backends is something like "MKL" or
+ * "CUDNN".
+ */
+enum class Backend {
+  CPU,
+  CUDA,
+  HIP,
+  VE,
+  FPGA,
+  IPU,
+  XPU,
+  SparseCPU,
+  SparseCUDA,
+  SparseCsrCPU,
+  SparseCsrCUDA,
+  SparseHIP,
+  SparseVE,
+  SparseXPU,
+  SparsePrivateUse1,
+  SparseCsrHIP,
+  SparseCsrVE,
+  SparseCsrXPU,
+  SparseCsrPrivateUse1,
+  MAIA,
+  XLA,
+  Vulkan,
+  Metal,
+  Meta,
+  QuantizedCPU,
+  QuantizedCUDA,
+  QuantizedXPU,
+  QuantizedPrivateUse1,
+  Undefined,
+  MkldnnCPU,
+  MPS,
+  HPU,
+  Lazy,
+  MTIA,
+  PrivateUse1,
+  NumOptions
+};
+
+inline Backend dispatchKeyToBackend(DispatchKey t) {
+  if (t == DispatchKey::CPU || t == DispatchKey::AutogradCPU) {
+    return Backend::CPU;
+  } else if (t == DispatchKey::CUDA || t == DispatchKey::AutogradCUDA) {
+    return Backend::CUDA;
+  } else if (t == DispatchKey::HIP) {
+    return Backend::HIP;
+  } else if (t == DispatchKey::VE) {
+    return Backend::VE;
+  } else if (t == DispatchKey::FPGA) {
+    return Backend::FPGA;
+  } else if (t == DispatchKey::MAIA) {
+    return Backend::MAIA;
+  } else if (t == DispatchKey::XLA || t == DispatchKey::AutogradXLA) {
+    return Backend::XLA;
+  } else if (t == DispatchKey::Lazy || t == DispatchKey::AutogradLazy) {
+    return Backend::Lazy;
+  } else if (t == DispatchKey::MPS || t == DispatchKey::AutogradMPS) {
+    return Backend::MPS;
+  } else if (t == DispatchKey::Vulkan) {
+    return Backend::Vulkan;
+  } else if (t == DispatchKey::Metal) {
+    return Backend::Metal;
+  } else if (t == DispatchKey::Meta) {
+    return Backend::Meta;
+  } else if (t == DispatchKey::SparseCPU) {
+    return Backend::SparseCPU;
+  } else if (t == DispatchKey::SparseCUDA) {
+    return Backend::SparseCUDA;
+  } else if (t == DispatchKey::SparseHIP) {
+    return Backend::SparseHIP;
+  } else if (t == DispatchKey::SparseVE) {
+    return Backend::SparseVE;
+  } else if (t == DispatchKey::SparsePrivateUse1) {
+    return Backend::SparsePrivateUse1;
+  } else if (t == DispatchKey::SparseCsrCPU) {
+    return Backend::SparseCsrCPU;
+  } else if (t == DispatchKey::SparseCsrCUDA) {
+    return Backend::SparseCsrCUDA;
+  } else if (t == DispatchKey::SparseCsrHIP) {
+    return Backend::SparseCsrHIP;
+  } else if (t == DispatchKey::SparseCsrVE) {
+    return Backend::SparseCsrVE;
+  } else if (t == DispatchKey::SparseCsrPrivateUse1) {
+    return Backend::SparseCsrPrivateUse1;
+  } else if (t == DispatchKey::MkldnnCPU) {
+    return Backend::MkldnnCPU;
+  } else if (t == DispatchKey::QuantizedCPU) {
+    return Backend::QuantizedCPU;
+  } else if (t == DispatchKey::QuantizedCUDA) {
+    return Backend::QuantizedCUDA;
+  } else if (t == DispatchKey::IPU || t == DispatchKey::AutogradIPU) {
+    return Backend::IPU;
+  } else if (t == DispatchKey::XPU || t == DispatchKey::AutogradXPU) {
+    return Backend::XPU;
+  } else if (t == DispatchKey::SparseXPU) {
+    return Backend::SparseXPU;
+  } else if (t == DispatchKey::SparseCsrXPU) {
+    return Backend::SparseCsrXPU;
+  } else if (t == DispatchKey::QuantizedXPU) {
+    return Backend::QuantizedXPU;
+  } else if (t == DispatchKey::QuantizedPrivateUse1) {
+    return Backend::QuantizedPrivateUse1;
+  } else if (t == DispatchKey::HPU || t == DispatchKey::AutogradHPU) {
+    return Backend::HPU;
+  } else if (t == DispatchKey::MTIA || t == DispatchKey::AutogradMTIA) {
+    return Backend::MTIA;
+  } else if (
+      t == DispatchKey::PrivateUse1 || t == DispatchKey::AutogradPrivateUse1) {
+    return Backend::PrivateUse1;
+  } else if (t == DispatchKey::Undefined) {
+    return Backend::Undefined;
+  } else {
+    TORCH_CHECK(false, "Unrecognized tensor type ID: ", t);
+  }
+}
+
+inline DispatchKey backendToDispatchKey(Backend b) {
+  switch (b) {
+    case Backend::CPU:
+      return DispatchKey::CPU;
+    case Backend::CUDA:
+      return DispatchKey::CUDA;
+    case Backend::HIP:
+      return DispatchKey::HIP;
+    case Backend::VE:
+      return DispatchKey::VE;
+    case Backend::FPGA:
+      return DispatchKey::FPGA;
+    case Backend::MAIA:
+      return DispatchKey::MAIA;
+    case Backend::XLA:
+      return DispatchKey::XLA;
+    case Backend::Lazy:
+      return DispatchKey::Lazy;
+    case Backend::IPU:
+      return DispatchKey::IPU;
+    case Backend::XPU:
+      return DispatchKey::XPU;
+    case Backend::SparseXPU:
+      return DispatchKey::SparseXPU;
+    case Backend::SparseCsrXPU:
+      return DispatchKey::SparseCsrXPU;
+    case Backend::SparseCPU:
+      return DispatchKey::SparseCPU;
+    case Backend::SparseCUDA:
+      return DispatchKey::SparseCUDA;
+    case Backend::SparseHIP:
+      return DispatchKey::SparseHIP;
+    case Backend::SparseVE:
+      return DispatchKey::SparseVE;
+    case Backend::SparsePrivateUse1:
+      return DispatchKey::SparsePrivateUse1;
+    case Backend::SparseCsrCPU:
+      return DispatchKey::SparseCsrCPU;
+    case Backend::SparseCsrCUDA:
+      return DispatchKey::SparseCsrCUDA;
+    case Backend::SparseCsrHIP:
+      return DispatchKey::SparseCsrHIP;
+    case Backend::SparseCsrVE:
+      return DispatchKey::SparseCsrVE;
+    case Backend::SparseCsrPrivateUse1:
+      return DispatchKey::SparseCsrPrivateUse1;
+    case Backend::MkldnnCPU:
+      return DispatchKey::MkldnnCPU;
+    case Backend::Vulkan:
+      return DispatchKey::Vulkan;
+    case Backend::Metal:
+      return DispatchKey::Metal;
+    case Backend::Meta:
+      return DispatchKey::Meta;
+    case Backend::QuantizedCPU:
+      return DispatchKey::QuantizedCPU;
+    case Backend::QuantizedCUDA:
+      return DispatchKey::QuantizedCUDA;
+    case Backend::QuantizedPrivateUse1:
+      return DispatchKey::QuantizedPrivateUse1;
+    case Backend::Undefined:
+      return DispatchKey::Undefined;
+    case Backend::MPS:
+      return DispatchKey::MPS;
+    case Backend::HPU:
+      return DispatchKey::HPU;
+    case Backend::MTIA:
+      return DispatchKey::MTIA;
+    case Backend::PrivateUse1:
+      return DispatchKey::PrivateUse1;
+    default:
+      throw std::runtime_error("Unknown backend");
+  }
+}
+
+inline DeviceType backendToDeviceType(Backend b) {
+  switch (b) {
+    case Backend::CPU:
+    case Backend::MkldnnCPU:
+    case Backend::SparseCPU:
+    case Backend::SparseCsrCPU:
+    case Backend::QuantizedCPU:
+      return DeviceType::CPU;
+    case Backend::CUDA:
+    case Backend::SparseCUDA:
+    case Backend::QuantizedCUDA:
+    case Backend::SparseCsrCUDA:
+      return DeviceType::CUDA;
+    case Backend::HIP:
+      return DeviceType::HIP;
+    case Backend::VE:
+      return DeviceType::VE;
+    case Backend::FPGA:
+      return DeviceType::FPGA;
+    case Backend::MAIA:
+      return DeviceType::MAIA;
+    case Backend::XLA:
+      return DeviceType::XLA;
+    case Backend::Lazy:
+      return DeviceType::Lazy;
+    case Backend::SparseHIP:
+      return DeviceType::HIP;
+    case Backend::SparseVE:
+      return DeviceType::VE;
+    case Backend::SparseCsrHIP:
+      return DeviceType::HIP;
+    case Backend::SparseCsrVE:
+      return DeviceType::VE;
+    case Backend::IPU:
+      return DeviceType::IPU;
+    case Backend::XPU:
+    case Backend::SparseXPU:
+    case Backend::SparseCsrXPU:
+    case Backend::QuantizedXPU:
+      return DeviceType::XPU;
+    case Backend::Vulkan:
+      return DeviceType::Vulkan;
+    case Backend::Metal:
+      return DeviceType::Metal;
+    case Backend::Meta:
+      return DeviceType::Meta;
+    case Backend::MPS:
+      return DeviceType::MPS;
+    case Backend::HPU:
+      return DeviceType::HPU;
+    case Backend::MTIA:
+      return DeviceType::MTIA;
+    case Backend::PrivateUse1:
+    case Backend::SparsePrivateUse1:
+    case Backend::SparseCsrPrivateUse1:
+    case Backend::QuantizedPrivateUse1:
+      return DeviceType::PrivateUse1;
+    case Backend::Undefined:
+      TORCH_CHECK(false, "Undefined backend is not a valid device type");
+    default:
+      TORCH_CHECK(false, "Unknown backend");
+  }
+}
+
+inline const char* toString(Backend b) {
+  switch (b) {
+    case Backend::CPU:
+      return "CPU";
+    case Backend::CUDA:
+      return "CUDA";
+    case Backend::HIP:
+      return "HIP";
+    case Backend::VE:
+      return "VE";
+    case Backend::FPGA:
+      return "FPGA";
+    case Backend::XPU:
+      return "XPU";
+    case Backend::IPU:
+      return "IPU";
+    case Backend::MAIA:
+      return "MAIA";
+    case Backend::XLA:
+      return "XLA";
+    case Backend::Lazy:
+      return "Lazy";
+    case Backend::MPS:
+      return "MPS";
+    case Backend::SparseCPU:
+      return "SparseCPU";
+    case Backend::SparseCUDA:
+      return "SparseCUDA";
+    case Backend::SparseHIP:
+      return "SparseHIP";
+    case Backend::SparseVE:
+      return "SparseVE";
+    case Backend::SparseXPU:
+      return "SparseXPU";
+    case Backend::SparsePrivateUse1:
+      return "SparsePrivateUse1";
+    case Backend::SparseCsrCPU:
+      return "SparseCsrCPU";
+    case Backend::SparseCsrCUDA:
+      return "SparseCsrCUDA";
+    case Backend::SparseCsrHIP:
+      return "SparseCsrHIP";
+    case Backend::SparseCsrVE:
+      return "SparseCsrVE";
+    case Backend::SparseCsrXPU:
+      return "SparseCsrXPU";
+    case Backend::SparseCsrPrivateUse1:
+      return "SparseCsrPrivateUse1";
+    case Backend::MkldnnCPU:
+      return "MkldnnCPU";
+    case Backend::Vulkan:
+      return "Vulkan";
+    case Backend::Metal:
+      return "Metal";
+    case Backend::Meta:
+      return "Meta";
+    case Backend::QuantizedCPU:
+      return "QuantizedCPU";
+    case Backend::QuantizedCUDA:
+      return "QuantizedCUDA";
+    case Backend::QuantizedXPU:
+      return "QuantizedXPU";
+    case Backend::QuantizedPrivateUse1:
+      return "QuantizedPrivateUse1";
+    case Backend::HPU:
+      return "HPU";
+    case Backend::MTIA:
+      return "MTIA";
+    case Backend::PrivateUse1:
+      return "PrivateUseOne";
+    default:
+      return "UNKNOWN_BACKEND";
+  }
+}
+
+inline bool isSparse(Backend b) {
+  switch (b) {
+    case Backend::SparseXPU:
+    case Backend::SparseCPU:
+    case Backend::SparseCUDA:
+    case Backend::SparseHIP:
+    case Backend::SparseVE:
+    case Backend::SparsePrivateUse1:
+      return true;
+    default:
+      return false;
+  }
+}
+
+inline bool isSparseCsr(Backend b) {
+  switch (b) {
+    case Backend::SparseCsrXPU:
+    case Backend::SparseCsrCPU:
+    case Backend::SparseCsrCUDA:
+    case Backend::SparseCsrHIP:
+    case Backend::SparseCsrVE:
+    case Backend::SparseCsrPrivateUse1:
+      return true;
+    default:
+      return false;
+  }
+}
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/CPUAllocator.h

@@ -0,0 +1,59 @@
+#pragma once
+
+#include <cstdint>
+#include <cstring>
+#include <mutex>
+#include <unordered_map>
+
+#include <c10/core/Allocator.h>
+#include <c10/macros/Export.h>
+#include <c10/util/Flags.h>
+
+// TODO: rename to c10
+C10_DECLARE_bool(caffe2_report_cpu_memory_usage);
+
+namespace c10 {
+
+using MemoryDeleter = void (*)(void*);
+
+// A helper function that is basically doing nothing.
+C10_API void NoDelete(void*);
+
+// A simple struct that is used to report C10's memory allocation,
+// deallocation status and out-of-memory events to the profiler
+class C10_API ProfiledCPUMemoryReporter {
+ public:
+  ProfiledCPUMemoryReporter() = default;
+  void New(void* ptr, size_t nbytes);
+  void OutOfMemory(size_t nbytes);
+  void Delete(void* ptr);
+
+ private:
+  std::mutex mutex_;
+  std::unordered_map<void*, size_t> size_table_;
+  size_t allocated_ = 0;
+  size_t log_cnt_ = 0;
+};
+
+C10_API ProfiledCPUMemoryReporter& profiledCPUMemoryReporter();
+
+// Get the CPU Allocator.
+C10_API at::Allocator* GetCPUAllocator();
+// Sets the CPU allocator to the given allocator: the caller gives away the
+// ownership of the pointer.
+C10_API void SetCPUAllocator(at::Allocator* alloc, uint8_t priority = 0);
+
+// Get the Default CPU Allocator
+C10_API at::Allocator* GetDefaultCPUAllocator();
+
+// Get the Default Mobile CPU Allocator
+C10_API at::Allocator* GetDefaultMobileCPUAllocator();
+
+// The CPUCachingAllocator is experimental and might disappear in the future.
+// The only place that uses it is in StaticRuntime.
+// Set the CPU Caching Allocator
+C10_API void SetCPUCachingAllocator(Allocator* alloc, uint8_t priority = 0);
+// Get the CPU Caching Allocator
+C10_API Allocator* GetCPUCachingAllocator();
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/CompileTimeFunctionPointer.h

@@ -0,0 +1,57 @@
+#pragma once
+
+#include <c10/util/TypeTraits.h>
+#include <type_traits>
+
+namespace c10 {
+
+/**
+ * Represent a function pointer as a C++ type.
+ * This allows using the function pointer as a type
+ * in a template and calling it from inside the template
+ * allows the compiler to inline the call because it
+ * knows the function pointer at compile time.
+ *
+ * Example 1:
+ *  int add(int a, int b) {return a + b;}
+ *  using Add = TORCH_FN_TYPE(add);
+ *  template<class Func> struct Executor {
+ *    int execute(int a, int b) {
+ *      return Func::func_ptr()(a, b);
+ *    }
+ *  };
+ *  Executor<Add> executor;
+ *  EXPECT_EQ(3, executor.execute(1, 2));
+ *
+ * Example 2:
+ *  int add(int a, int b) {return a + b;}
+ *  template<class Func> int execute(Func, int a, int b) {
+ *    return Func::func_ptr()(a, b);
+ *  }
+ *  EXPECT_EQ(3, execute(TORCH_FN(add), 1, 2));
+ */
+template <class FuncType_, FuncType_* func_ptr_>
+struct CompileTimeFunctionPointer final {
+  static_assert(
+      guts::is_function_type<FuncType_>::value,
+      "TORCH_FN can only wrap function types.");
+  using FuncType = FuncType_;
+
+  static constexpr FuncType* func_ptr() {
+    return func_ptr_;
+  }
+};
+
+template <class T>
+struct is_compile_time_function_pointer : std::false_type {};
+template <class FuncType, FuncType* func_ptr>
+struct is_compile_time_function_pointer<
+    CompileTimeFunctionPointer<FuncType, func_ptr>> : std::true_type {};
+
+} // namespace c10
+
+#define TORCH_FN_TYPE(func)                                           \
+  ::c10::CompileTimeFunctionPointer<                                  \
+      std::remove_pointer_t<std::remove_reference_t<decltype(func)>>, \
+      func>
+#define TORCH_FN(func) TORCH_FN_TYPE(func)()

parrot/lib/python3.10/site-packages/torch/include/c10/core/Contiguity.h

@@ -0,0 +1,129 @@
+#pragma once
+#include <c10/core/SymBool.h>
+#include <c10/core/SymInt.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/SmallVector.h>
+#include <c10/util/irange.h>
+
+#include <algorithm>
+#include <cstdint>
+
+namespace c10 {
+
+template <typename T>
+bool _compute_contiguous(ArrayRef<T> sizes, ArrayRef<T> strides, T numel) {
+  bool is_contiguous = true;
+  if (TORCH_GUARD_SIZE_OBLIVIOUS(sym_eq(numel, 0))) {
+    return is_contiguous;
+  }
+  T z = 1;
+  // NB: make sure we do signed arithmetic
+  for (int64_t d = int64_t(sizes.size()) - 1; d >= 0; d--) {
+    const auto& size_d = sizes[d];
+    if (TORCH_GUARD_SIZE_OBLIVIOUS(sym_ne(size_d, 1))) {
+      if (TORCH_GUARD_SIZE_OBLIVIOUS(sym_eq(strides[d], z))) {
+        z *= size_d;
+      } else {
+        is_contiguous = false;
+        break;
+      }
+    }
+  }
+  return is_contiguous;
+}
+
+template <typename T>
+bool _compute_channels_last_contiguous_2d(
+    ArrayRef<T> sizes,
+    ArrayRef<T> strides) {
+  // Please don't combine these code, constant array is used here to let
+  // compiler fully unroll the loop to get better performance
+  switch (sizes.size()) {
+    case 4: {
+      T expected = 1;
+      for (auto& d : {1, 3, 2, 0}) {
+        const auto& size_d = sizes[d];
+        if (TORCH_GUARD_SIZE_OBLIVIOUS(sym_ne(size_d, 1))) {
+          if (TORCH_GUARD_SIZE_OBLIVIOUS(sym_ne(strides[d], expected))) {
+            return false;
+          }
+          expected *= size_d;
+        }
+      }
+      return true;
+    }
+      // NOLINTNEXTLINE(bugprone-branch-clone)
+    case 3:
+      // TODO dim == 3 case will be enabled once it is fully tested
+      return false;
+    default:
+      return false;
+  }
+}
+
+template <typename T>
+bool _compute_channels_last_contiguous_3d(
+    ArrayRef<T> sizes,
+    ArrayRef<T> strides) {
+  // Please don't combine these code, constant array is used here to let
+  // compiler fully unroll the loop to get better performance
+  switch (sizes.size()) {
+    case 5: {
+      T expected = 1;
+      for (auto& d : {1, 4, 3, 2, 0}) {
+        const auto& size_d = sizes[d];
+        if (TORCH_GUARD_SIZE_OBLIVIOUS(sym_ne(size_d, 1))) {
+          if (TORCH_GUARD_SIZE_OBLIVIOUS(sym_ne(strides[d], expected))) {
+            return false;
+          }
+          expected *= size_d;
+        }
+      }
+      return true;
+    }
+      // NOLINTNEXTLINE(bugprone-branch-clone)
+    case 4:
+      // TODO dim == 4 case will be enabled once it is fully tested
+      return false;
+    default:
+      return false;
+  }
+}
+
+template <typename T>
+bool _compute_non_overlapping_and_dense(
+    ArrayRef<T> sizes,
+    ArrayRef<T> strides) {
+  auto dim = sizes.size();
+  if (dim == 1) {
+    return sizes[0] < 2 || strides[0] == 1;
+  }
+  SmallVector<int64_t, 5> perm;
+  perm.resize(dim);
+  for (const auto i : c10::irange(dim)) {
+    perm[i] = i;
+  }
+  // Sort by strides, leaving 0 and 1 sized dims at the end of the array
+  std::sort(perm.begin(), perm.end(), [&](int64_t a, int64_t b) {
+    if (sizes[a] < 2) {
+      return false;
+    } else if (sizes[b] < 2) {
+      return true;
+    }
+    return strides[a] < strides[b];
+  });
+  T require_stride = 1;
+  for (const auto i : c10::irange(dim)) {
+    const auto& size_perm_i = sizes[perm[i]];
+    if (size_perm_i < 2) {
+      return true;
+    }
+    if (strides[perm[i]] != require_stride) {
+      return false;
+    }
+    require_stride *= size_perm_i;
+  }
+  return true;
+}
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/CopyBytes.h

@@ -0,0 +1,48 @@
+#pragma once
+
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <cstddef>
+
+namespace c10 {
+
+using CopyBytesFunction = void (*)(
+    size_t nbytes,
+    const void* src,
+    Device src_device,
+    void* dst,
+    Device dst_device);
+
+struct C10_API _CopyBytesFunctionRegisterer {
+  _CopyBytesFunctionRegisterer(
+      DeviceType from,
+      DeviceType to,
+      CopyBytesFunction func_sync,
+      CopyBytesFunction func_async = nullptr);
+};
+
+#define REGISTER_COPY_BYTES_FUNCTION(from, to, ...)           \
+  namespace {                                                 \
+  static _CopyBytesFunctionRegisterer C10_ANONYMOUS_VARIABLE( \
+      g_copy_function)(from, to, __VA_ARGS__);                \
+  }
+
+/*
+ * WARNING: Implementations for this function are currently registered from
+ * ATen and caffe2, not yet from c10. Don't use this if not either ATen
+ * or caffe2 is present as well.
+ * We can't move them yet, because the CUDA implementations aren't unified yet
+ * between ATen and caffe2.
+ * We're planning to move the implementations into c10/backend/xxx
+ * to make c10 self contained again.
+ */
+C10_API void CopyBytes(
+    size_t nbytes,
+    const void* src,
+    Device src_device,
+    void* dst,
+    Device dst_device,
+    bool async);
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/DefaultDtype.h

@@ -0,0 +1,15 @@
+#pragma once
+
+#include <c10/core/ScalarType.h>
+#include <c10/macros/Export.h>
+
+namespace caffe2 {
+class TypeMeta;
+} // namespace caffe2
+
+namespace c10 {
+C10_API void set_default_dtype(caffe2::TypeMeta dtype);
+C10_API const caffe2::TypeMeta get_default_dtype();
+C10_API ScalarType get_default_dtype_as_scalartype();
+C10_API const caffe2::TypeMeta get_default_complex_dtype();
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/DefaultTensorOptions.h

@@ -0,0 +1,45 @@
+#pragma once
+
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/core/Layout.h>
+#include <c10/core/ScalarType.h>
+#include <c10/util/typeid.h>
+
+namespace c10 {
+
+struct TensorOptions;
+
+/// Like TensorOptions, but all fields are guaranteed to be filled.
+struct DefaultTensorOptions {
+  DefaultTensorOptions() = default;
+
+  caffe2::TypeMeta dtype() const noexcept {
+    return dtype_;
+  }
+  Device device() const noexcept {
+    return device_;
+  }
+  Layout layout() const noexcept {
+    return layout_;
+  }
+  bool requires_grad() const noexcept {
+    return requires_grad_;
+  }
+
+  // Defined in TensorOptions.h
+  inline DefaultTensorOptions& merge(const TensorOptions& options);
+
+ private:
+  caffe2::TypeMeta dtype_ = caffe2::TypeMeta::Make<float>(); // 64-bit
+  Device device_ = at::kCPU; // 32-bit
+  Layout layout_ = at::kStrided; // 8-bit
+  bool requires_grad_ = false; // 8-bit
+};
+
+inline const DefaultTensorOptions& getDefaultTensorOptions() {
+  static const auto options = DefaultTensorOptions();
+  return options;
+}
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/Device.h

@@ -0,0 +1,216 @@
+#pragma once
+
+#include <c10/core/DeviceType.h>
+#include <c10/macros/Export.h>
+#include <c10/util/Exception.h>
+
+#include <cstddef>
+#include <cstdint>
+#include <functional>
+#include <iosfwd>
+#include <string>
+
+namespace c10 {
+
+/// An index representing a specific device; e.g., the 1 in GPU 1.
+/// A DeviceIndex is not independently meaningful without knowing
+/// the DeviceType it is associated; try to use Device rather than
+/// DeviceIndex directly.
+using DeviceIndex = int8_t;
+
+/// Represents a compute device on which a tensor is located. A device is
+/// uniquely identified by a type, which specifies the type of machine it is
+/// (e.g. CPU or CUDA GPU), and a device index or ordinal, which identifies the
+/// specific compute device when there is more than one of a certain type. The
+/// device index is optional, and in its defaulted state represents (abstractly)
+/// "the current device". Further, there are two constraints on the value of the
+/// device index, if one is explicitly stored:
+/// 1. A negative index represents the current device, a non-negative index
+/// represents a specific, concrete device,
+/// 2. When the device type is CPU, the device index must be zero.
+struct C10_API Device final {
+  using Type = DeviceType;
+
+  /// Constructs a new `Device` from a `DeviceType` and an optional device
+  /// index.
+  /* implicit */ Device(DeviceType type, DeviceIndex index = -1)
+      : type_(type), index_(index) {
+    validate();
+  }
+
+  /// Constructs a `Device` from a string description, for convenience.
+  /// The string supplied must follow the following schema:
+  /// `(cpu|cuda)[:<device-index>]`
+  /// where `cpu` or `cuda` specifies the device type, and
+  /// `:<device-index>` optionally specifies a device index.
+  /* implicit */ Device(const std::string& device_string);
+
+  /// Returns true if the type and index of this `Device` matches that of
+  /// `other`.
+  bool operator==(const Device& other) const noexcept {
+    return this->type_ == other.type_ && this->index_ == other.index_;
+  }
+
+  /// Returns true if the type or index of this `Device` differs from that of
+  /// `other`.
+  bool operator!=(const Device& other) const noexcept {
+    return !(*this == other);
+  }
+
+  /// Sets the device index.
+  void set_index(DeviceIndex index) {
+    index_ = index;
+  }
+
+  /// Returns the type of device this is.
+  DeviceType type() const noexcept {
+    return type_;
+  }
+
+  /// Returns the optional index.
+  DeviceIndex index() const noexcept {
+    return index_;
+  }
+
+  /// Returns true if the device has a non-default index.
+  bool has_index() const noexcept {
+    return index_ != -1;
+  }
+
+  /// Return true if the device is of CUDA type.
+  bool is_cuda() const noexcept {
+    return type_ == DeviceType::CUDA;
+  }
+
+  /// Return true if the device is of PrivateUse1 type.
+  bool is_privateuseone() const noexcept {
+    return type_ == DeviceType::PrivateUse1;
+  }
+
+  /// Return true if the device is of MPS type.
+  bool is_mps() const noexcept {
+    return type_ == DeviceType::MPS;
+  }
+
+  /// Return true if the device is of HIP type.
+  bool is_hip() const noexcept {
+    return type_ == DeviceType::HIP;
+  }
+
+  /// Return true if the device is of VE type.
+  bool is_ve() const noexcept {
+    return type_ == DeviceType::VE;
+  }
+
+  /// Return true if the device is of XPU type.
+  bool is_xpu() const noexcept {
+    return type_ == DeviceType::XPU;
+  }
+
+  /// Return true if the device is of IPU type.
+  bool is_ipu() const noexcept {
+    return type_ == DeviceType::IPU;
+  }
+
+  /// Return true if the device is of XLA type.
+  bool is_xla() const noexcept {
+    return type_ == DeviceType::XLA;
+  }
+
+  /// Return true if the device is of MTIA type.
+  bool is_mtia() const noexcept {
+    return type_ == DeviceType::MTIA;
+  }
+
+  /// Return true if the device is of HPU type.
+  bool is_hpu() const noexcept {
+    return type_ == DeviceType::HPU;
+  }
+
+  /// Return true if the device is of Lazy type.
+  bool is_lazy() const noexcept {
+    return type_ == DeviceType::Lazy;
+  }
+
+  /// Return true if the device is of Vulkan type.
+  bool is_vulkan() const noexcept {
+    return type_ == DeviceType::Vulkan;
+  }
+
+  /// Return true if the device is of Metal type.
+  bool is_metal() const noexcept {
+    return type_ == DeviceType::Metal;
+  }
+
+  /// Return true if the device is of MAIA type.
+  bool is_maia() const noexcept {
+    return type_ == DeviceType::MAIA;
+  }
+
+  /// Return true if the device is of META type.
+  bool is_meta() const noexcept {
+    return type_ == DeviceType::Meta;
+  }
+
+  /// Return true if the device is of CPU type.
+  bool is_cpu() const noexcept {
+    return type_ == DeviceType::CPU;
+  }
+
+  /// Return true if the device supports arbitrary strides.
+  bool supports_as_strided() const noexcept {
+    return type_ != DeviceType::IPU && type_ != DeviceType::XLA &&
+        type_ != DeviceType::Lazy && type_ != DeviceType::MTIA;
+  }
+
+  /// Same string as returned from operator<<.
+  std::string str() const;
+
+ private:
+  DeviceType type_;
+  DeviceIndex index_ = -1;
+  void validate() {
+    // Removing these checks in release builds noticeably improves
+    // performance in micro-benchmarks.
+    // This is safe to do, because backends that use the DeviceIndex
+    // have a later check when we actually try to switch to that device.
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        index_ >= -1,
+        "Device index must be -1 or non-negative, got ",
+        static_cast<int>(index_));
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        !is_cpu() || index_ <= 0,
+        "CPU device index must be -1 or zero, got ",
+        static_cast<int>(index_));
+  }
+};
+
+C10_API std::ostream& operator<<(std::ostream& stream, const Device& device);
+
+} // namespace c10
+
+namespace std {
+template <>
+struct hash<c10::Device> {
+  size_t operator()(c10::Device d) const noexcept {
+    // Are you here because this static assert failed?  Make sure you ensure
+    // that the bitmasking code below is updated accordingly!
+    static_assert(sizeof(c10::DeviceType) == 1, "DeviceType is not 8-bit");
+    static_assert(sizeof(c10::DeviceIndex) == 1, "DeviceIndex is not 8-bit");
+    // Note [Hazard when concatenating signed integers]
+    // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+    // We must first convert to a same-sized unsigned type, before promoting to
+    // the result type, to prevent sign extension when any of the values is -1.
+    // If sign extension occurs, you'll clobber all of the values in the MSB
+    // half of the resulting integer.
+    //
+    // Technically, by C/C++ integer promotion rules, we only need one of the
+    // uint32_t casts to the result type, but we put in both for explicitness's
+    // sake.
+    uint32_t bits = static_cast<uint32_t>(static_cast<uint8_t>(d.type()))
+            << 16 |
+        static_cast<uint32_t>(static_cast<uint8_t>(d.index()));
+    return std::hash<uint32_t>{}(bits);
+  }
+};
+} // namespace std

parrot/lib/python3.10/site-packages/torch/include/c10/core/DeviceArray.h

@@ -0,0 +1,28 @@
+#include <c10/core/Allocator.h>
+#include <c10/util/Exception.h>
+#include <cstddef>
+#include <cstdint>
+#include <type_traits>
+
+namespace c10 {
+
+template <typename T>
+class DeviceArray {
+ public:
+  DeviceArray(c10::Allocator& allocator, size_t size)
+      : data_ptr_(allocator.allocate(size * sizeof(T))) {
+    static_assert(std::is_trivial<T>::value, "T must be a trivial type");
+    TORCH_INTERNAL_ASSERT(
+        0 == (reinterpret_cast<intptr_t>(data_ptr_.get()) % alignof(T)),
+        "c10::DeviceArray: Allocated memory is not aligned for this data type");
+  }
+
+  T* get() {
+    return static_cast<T*>(data_ptr_.get());
+  }
+
+ private:
+  c10::DataPtr data_ptr_;
+};
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/DispatchKey.h

@@ -0,0 +1,746 @@
+#pragma once
+
+#include <c10/core/DeviceType.h>
+#include <c10/macros/Export.h>
+#include <cstddef>
+#include <cstdint>
+#include <functional>
+#include <ostream>
+#include <string>
+
+namespace c10 {
+
+// Semantically, each value of BackendComponent identifies a "backend" for our
+// dispatch. Some functionalities that we may dispatch to are allowed to
+// register different handlers for each backend. The BackendComponent is then
+// used to figure out which backend implementation to dispatch to.
+
+// In implementation terms, the backend component identifies a specific "bit" in
+// a DispatchKeySet. The bits in the DispatchKeySet are split between the bottom
+// ~12 "BackendComponent" bits, while the remaining upper bits are assigned to
+// functionalities. When we encounter a functionality bit that is known to be
+// customizable per-backend, then we also look at the lower BackendComponent
+// bits and take the highest bit to determine which backend's implementation to
+// use.
+
+// WARNING!  If you add a new backend component to the end of this list,
+// make sure you register it before Meta.
+// Meta must be at the end so that meta key in tls triggers meta kernels.
+// (But you shouldn't: private use keys should have higher precedence than all
+// built-in keys)
+
+// If you add a new (non-privateuse) backend here,
+// make sure to add an Autograd<Backend> fallthrough kernel
+// in aten/src/ATen/core/VariableFallbackKernel.cpp
+
+#define C10_FORALL_BACKEND_COMPONENTS(_, extra) \
+  _(CPU, extra)                                 \
+  _(CUDA, extra)                                \
+  _(HIP, extra)                                 \
+  _(XLA, extra)                                 \
+  _(MPS, extra)                                 \
+  _(IPU, extra)                                 \
+  _(XPU, extra)                                 \
+  _(HPU, extra)                                 \
+  _(VE, extra)                                  \
+  _(Lazy, extra)                                \
+  _(MTIA, extra)                                \
+  _(PrivateUse1, extra)                         \
+  _(PrivateUse2, extra)                         \
+  _(PrivateUse3, extra)                         \
+  _(Meta, extra)
+
+// WARNING!  If we add a new per-backend functionality key that has higher
+// priority than Autograd, then make sure you update EndOfRuntimeBackendKeys
+
+#define C10_FORALL_FUNCTIONALITY_KEYS(_) \
+  _(Dense, )                             \
+  _(Quantized, Quantized)                \
+  _(Sparse, Sparse)                      \
+  _(SparseCsr, SparseCsr)                \
+  _(NestedTensor, NestedTensor)          \
+  _(AutogradFunctionality, Autograd)
+
+enum class BackendComponent : uint8_t {
+
+  // A "backend" is colloquially used to refer to handlers for dispatch
+  // which actually implement the numerics of an operation in question.
+  //
+  // Due to the nature of the enum, these backends are specified in
+  // an ordered way, but for most backends this order is not semantically
+  // meaningful (e.g., it's valid to reorder these backends without changing
+  // semantics).  The only situation when backend ordering is meaningful
+  // is when the backend participates in multiple dispatch with another
+  // backend; e.g., CPU and CUDA (cuda must have higher priority).
+
+  // These keys don't correspond to individual kernels.
+  // Instead, they represent the backends that are allowed to override specific
+  // pieces of functionality:
+  // - dense kernels (e.g. DispatchKey::CPU)
+  // - sparse kernels (e.g. DispatchKey::SparseCPU)
+  // - quantized kernels (e.g. DispatchKey::QuantizedCPU)
+  // - autograd kernels (e.g. DispatchKey::AutogradCPU)
+  // We reserve space in the runtime operator table for this full cross product
+  // of
+  // [backends in this enum] x [keys below that are explicitly marked as having
+  // per-backend functionality]
+  //
+  // A meta tensor is a tensor without any data associated with it.  (They
+  // have also colloquially been referred to as tensors on the "null" device).
+  // A meta tensor can be used to dry run operators without actually doing any
+  // computation, e.g., add on two meta tensors would give you another meta
+  // tensor with the output shape and dtype, but wouldn't actually add anything.
+
+  InvalidBit = 0,
+#define DEFINE_BACKEND_COMPONENT(n, _) n##Bit,
+  C10_FORALL_BACKEND_COMPONENTS(DEFINE_BACKEND_COMPONENT, unused)
+#undef DEFINE_BACKEND_COMPONENT
+
+  // Define an alias to represent end of backend dispatch keys.
+  // If you add new backend keys after PrivateUse3, please also update it here.
+  EndOfBackendKeys = MetaBit,
+};
+
+// Semantically, a dispatch key identifies a possible "level" in our
+// dispatch, for which a handler may be registered. Each handler corresponds
+// to a type of functionality.
+//
+// In implementation terms, the dispatch key identifies a specific "bit" in a
+// DispatchKeySet.  Higher bit indexes get handled by dispatching first (because
+// we "count leading zeros" when we extract the highest priority dispatch
+// key.)
+//
+// Note [DispatchKey Classification]
+// This enum actually contains several types of keys, which are explained
+// in more detail further down:
+// (1) non-customizable backends (e.g. FPGA)
+// (2) non-customizable functionalities (e.g. Functionalize)
+// (3) functionalized that are customizable per backend (e.g. Dense, Sparse,
+// AutogradFunctionality) (4) per-backend instances of customizable
+// functionalities (e.g. CPU, SparseCPU, AutogradCPU) (5) alias keys (e.g.
+// CompositeImplicitAutograd)
+//
+// Of the categories above, it's important to note:
+// (a) which keys are assigned individual bits in a DispatchKeySet
+// (b) which keys are assigned individual slots in the runtime operator table
+// ("Runtime keys")
+//
+// (1), (2) and (3) all get their own dedicated bits in the DispatchKeySet.
+// (1), (2) and (4) all get their own dedicated slots in the runtime operator
+// table.
+
+// See Note [DispatchKeySet Internal Representation] for more details.
+//
+// NOTE: Keep the list in sync with `DispatchKey` in torchgen/model.py
+enum class DispatchKey : uint16_t {
+
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~ UNDEFINED ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ //
+  // This is not a "real" functionality, but it exists to give us a "nullopt"
+  // element we can return for cases when a DispatchKeySet contains no elements.
+  // You can think a more semantically accurate definition of DispatchKey is:
+  //
+  //    using DispatchKey = optional<RealDispatchKey>
+  //
+  // and Undefined == nullopt.  We didn't actually represent
+  // it this way because optional<RealDispatchKey> would take two
+  // words, when DispatchKey fits in eight bits.
+
+  Undefined = 0,
+
+  // Define an alias for Undefined to represent CatchAll (long term
+  // this will get eliminated, but for now it's convenient)
+  CatchAll = Undefined,
+
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~ Functionality Keys ~~~~~~~~~~~~~~~~~~~~~~ //
+  // Every value in the enum (up to EndOfFunctionalityKeys)
+  // corresponds to an individual "functionality" that can be dispatched to.
+  // This is represented in the DispatchKeySet by assigning each of these enum
+  // values
+  // to each of the remaining (64 - len(BackendComponent)) bits.
+  //
+  // Most of these functionalities have a single handler assigned to them,
+  // making them "runtime keys".
+  // That map to a single slot in the runtime operator table.
+  //
+  // A few functionalities are allowed to be customizable per backend.
+  // See [Note: Per-Backend Functionality Dispatch Keys] for details.
+
+  // See [Note: Per-Backend Functionality Dispatch Keys]
+  Dense,
+
+  // Below are non-extensible backends.
+  // These are backends that currently don't have their own overrides for
+  // Autograd/Sparse/Quantized kernels,
+  // and we therefore don't waste space in the runtime operator table allocating
+  // space for them.
+  // If any of these backends ever need to customize, e.g., Autograd, then we'll
+  // need to add a DispatchKey::*Bit for them.
+
+  // TODO: put this in BackendComponents
+  FPGA, // Xilinx support lives out of tree at
+  // https://gitlab.com/pytorch-complex/vitis_kernels
+
+  // TODO: put this in BackendComponents
+  // MAIA backend lives out of tree
+  // - test/cpp_extensions/maia_extension.cpp
+  // - test/test_torch.py
+  // - aten/src/ATen/test/extension_backend_test.cpp
+  MAIA,
+
+  Vulkan, // TODO: put this in BackendComponents
+  Metal, // TODO: put this in BackendComponents
+
+  // See [Note: Per-Backend Functionality Dispatch Keys]
+  Quantized,
+
+  // This backend is to support custom RNGs; it lets you go
+  // to a different kernel if you pass in a generator that is not a
+  // traditional CPUGeneratorImpl/CUDAGeneratorImpl.  To make use of this
+  // key:
+  //  1) set it as a second parameter of at::Generator constructor call in
+  //     the user-defined PRNG class.
+  //  2) use it as a dispatch key while registering custom kernels
+  //     (templatized kernels specialized for user-defined PRNG class)
+  // intended for out of tree use; tested by aten/src/ATen/test/rng_test.cpp
+  CustomRNGKeyId,
+
+  // TODO: Make Mkldnn a functionality key, so we can give it Meta
+  // support
+  // Here are backends which specify more specialized operators
+  // based on the layout of the tensor.  Note that the sparse backends
+  // are one case where ordering matters: sparse multi-dispatches with
+  // the corresponding dense tensors, and must be handled before them.
+  MkldnnCPU, // registered at build/aten/src/ATen/RegisterMkldnnCPU.cpp
+  // NB: not to be confused with MKLDNN, which is Caffe2 only
+
+  // See [Note: Per-Backend Functionality Dispatch Keys]
+  Sparse,
+
+  SparseCsr,
+
+  NestedTensor,
+
+  // In some situations, it is not immediately obvious what the correct
+  // backend for function is, because the function in question doesn't
+  // have any "tensor" arguments.  In this case, a BackendSelect function
+  // can be registered to implement the custom determination of the
+  // correct backend.
+  BackendSelect,
+
+  Python,
+
+  // Out-of-core key for Fake Tensor in torchdistx.
+  // See https://pytorch.org/torchdistx/latest/fake_tensor.html
+  // TODO: delete this in favor of Python-implemented fake tensor
+  Fake,
+  // See Note [Out-of-tree vmap+grad prototype]. The purpose of this key
+  // is to insert code after the "autograd subsystem" runs, so this key should
+  // be directly after ADInplaceOrView and all of the autograd keys.
+  FuncTorchDynamicLayerBackMode,
+
+  // Alias and mutation removal.
+  // If some backends want to opt into only alias removal or only mutation
+  // removal,
+  // we can consider adding separate keys dedicated to those individual passes.
+  // See Note [Functionalization Pass In Core] for details.
+  Functionalize,
+
+  // The named dispatch key is set for any tensors with named dimensions.
+  // Although we have a dispatch key for named tensors, for historical reasons,
+  // this dispatch key doesn't do any of the substantive functionality for named
+  // tensor (though, hypothetically, it could!)  At the moment, it's just
+  // responsible for letting us give good error messages when operations
+  // don't support named tensors.
+  //
+  // NB: If you ever consider moving named tensor functionality into
+  // this dispatch key, note that it might be necessary add another dispatch
+  // key that triggers before composite operators, in case a composite operator
+  // has named dimension propagation that doesn't match that of its
+  // constituent parts.
+  // TODO: delete this once torchdim lands in functorch
+  Named,
+
+  // The Conjugate dispatch key is set for any tensors that need to perform
+  // conjugation
+  // This is implemented at a dispatch level right before any backends run
+  Conjugate,
+
+  // The Negative dispatch key is set for any tensors that need to perform
+  // negation
+  // This is implemented at a dispatch level right before any backends run
+  Negative,
+
+  ZeroTensor, // registered at build/aten/src/ATen/RegisterZeroTensor.cpp
+
+  // Note [ADInplaceOrView key]
+  // ADInplaceOrView key is used by inplace or view ops to register a kernel
+  // that does additional setup for future autograd computation.
+  //
+  // 1. For inplace ops this kernel does version bump
+  // 2. For view ops this kernel does `as_view` setup where we properly setup
+  //    DifferentiableViewMeta on the view tensors.
+  //
+  // For other ops it's fallthrough kernel since there's no extra
+  // work to do.
+  //
+  // Note [Dream: skip VariableType kernel when requires_grad=false]
+  //
+  // In an ideal world where we can skip VariableType kernel for inputs
+  // with requires_grad=false, instead of a fallthrough kernel, we'll
+  // register a kernel shown below to all functional ops as well:
+  // torch::Tensor my_functional_op(...) {
+  //   {
+  //     // Note for every op in VariableType, you need to go through
+  //     // `AutoDispatchBelowADInplaceOrView` guard exactly once to add the
+  //     // key to TLS excluded set. If you don't go through it at all,
+  //     // inplace/view ops called through `at::` inside your backend
+  //     // kernel will dispatch to ADInplaceOrView kernels and do a lot
+  //     // of extra work.
+  //     at::AutoDispatchBelowADInplaceOrView guard;
+  //     at::redispatch::my_functional_op(...);
+  //   }
+  // }
+  // But this work is currently blocked since it adds an extra dispatch
+  // for all ops and it's non-trivial overhead at model level(a few percents).
+  // Thus our current approach takes advantage of the fact every kernel go
+  // through VariableType kernel first and pulls the
+  // `at::AutoDispatchBelowADInplaceOrView` guard of functional ops
+  // up to the `VariableType` kernel. Thus we only add the extra dispatch
+  // to view/inplace ops to minimize its perf impact to real models.
+  ADInplaceOrView,
+  // Note [Alias Dispatch Key : Autograd]
+  // All backends are oblivious to autograd; autograd is handled as a
+  // layer which happens on top of all backends. It inspects the autograd
+  // metadata of all inputs, determines what autograd metadata should be
+  // constructed by the output, and otherwise defers to the backend to
+  // actually do the numeric computation.  Autograd contains
+  // the bulk of this logic.
+
+  // Autograd is now an alias dispatch key which by default maps to all
+  // backend-specific autograd keys.
+  // Backend-specific allow backends to override the default kernel registered
+  // to Autograd key as needed.
+  // For example, XLA wants to define autograd for einsum directly.
+  // Registering a custom autograd implementation at the XLA key won't work
+  // because we process Autograd before XLA.  This key has higher priority and
+  // gets processed first.  You generally should NOT redispatch after handling
+  // autograd here (since that would result in execution of the Autograd
+  // operator, which you're trying to skip).  In AutogradXLA implementations,
+  // you are responsible for handling autograd yourself, or deferring to other
+  // operators which support autograd.
+
+  // Currently we only have backend-specific autograd keys for CPU/CUDA/XLA and
+  // reserved user-defined backends. All other in-tree backends share the
+  // AutogradOther key. We can add specific autograd key for those backends
+  // upon request.
+  AutogradOther,
+
+  // See [Note: Per-Backend Functionality Dispatch Keys]
+  AutogradFunctionality,
+
+  // NestedTensor is an example of something that isn't a "real backend"
+  // (because it mostly consists of redispatching kernels)
+  // but it would like to override autograd functionality in C++.
+  // We can handle cases like this by adding an extra functionality key
+  // exclusively for handling autograd for NestedTensor.
+  // lives out of tree at
+  // https://github.com/pytorch/nestedtensor
+  AutogradNestedTensor,
+
+  Tracer,
+
+  // TODO: make Autocast a functionality key
+  // Autocasting precedes VariableTypeId, to ensure casts are autograd-exposed
+  // and inputs are saved for backward in the post-autocast type.
+  AutocastCPU,
+  AutocastXPU,
+  AutocastIPU,
+  AutocastHPU,
+  AutocastXLA,
+  // AutocastXLA is only being used for TPUs. XLA GPUs continue to use
+  // AutocastCUDA.
+  AutocastCUDA,
+  AutocastPrivateUse1,
+
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~ WRAPPERS ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ //
+  // There are a number of alternative modes which may want to handle before
+  // autograd; for example, error checking, tracing, profiling or vmap.  They
+  // go here.
+
+  FuncTorchBatched, // See Note [Out-of-tree vmap+grad prototype]
+
+  // Dispatch key for BatchedTensorImpl wrapping a nested tensor.
+  BatchedNestedTensor,
+
+  FuncTorchVmapMode, // See Note [Out-of-tree vmap+grad prototype]
+
+  // This is the dispatch key for BatchedTensorImpl, which is used to implement
+  // batching rules for vmap.
+  Batched,
+
+  // When we are inside a vmap, all tensors dispatch on this key.
+  // See Note: [DispatchKey::VmapMode usage] for more details.
+  VmapMode,
+
+  FuncTorchGradWrapper, // See Note [Out-of-tree vmap+grad prototype]
+
+  // Out-of-core key for Deferred Module Initialization in torchdistx.
+  // See https://pytorch.org/torchdistx/latest/deferred_init.html
+  DeferredInit,
+
+  // Used by Python key logic to know the set of tls on entry to the dispatcher
+  // This kernel assumes it is the top-most non-functorch-related DispatchKey.
+  // If you add a key above, make sure to update the fallback implementation for
+  // this.
+  PythonTLSSnapshot,
+
+  // This key should be at the very top of the dispatcher
+  FuncTorchDynamicLayerFrontMode, // See Note [Out-of-tree vmap+grad prototype]
+
+  // TESTING: This is intended to be a generic testing tensor type id.
+  // Don't use it for anything real; its only acceptable use is within a single
+  // process test.  Use it by creating a TensorImpl with this DispatchKey, and
+  // then registering operators to operate on this type id.  See
+  // aten/src/ATen/core/dispatch/backend_fallback_test.cpp for a usage example.
+  TESTING_ONLY_GenericWrapper,
+
+  // TESTING: This is intended to be a generic testing tensor type id.
+  // Don't use it for anything real; its only acceptable use is within a ingle
+  // process test.  Use it by toggling the mode on and off via
+  // TESTING_ONLY_tls_generic_mode_set_enabled and then registering operators
+  // to operate on this type id.  See
+  // aten/src/ATen/core/dispatch/backend_fallback_test.cpp
+  // for a usage example
+  TESTING_ONLY_GenericMode,
+
+  // This key is used for pre-dispatch tracing in make_fx.
+  // It has lower priority than the PythonDispatcher key
+  // because we use the PythonDispatcher to intercept the key from python,
+  // and avoid having to implement it in C++.
+  PreDispatch,
+
+  // This is a bypass that allows you to skip running the C++ dispatcher
+  // entirely
+  PythonDispatcher,
+
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ FIN ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ //
+  EndOfFunctionalityKeys, // End of functionality keys.
+
+// ~~~~~~~~~~~~~~ "Dense" Per-Backend Dispatch keys ~~~~~~~~~~~~~~~~~~~~ //
+// Here are backends which you think of as traditionally specifying
+// how to implement operations on some device.
+
+#define DEFINE_PER_BACKEND_KEYS_FOR_BACKEND(n, prefix) prefix##n,
+
+#define DEFINE_PER_BACKEND_KEYS(fullname, prefix)      \
+  StartOf##fullname##Backends,                         \
+      C10_FORALL_BACKEND_COMPONENTS(                   \
+          DEFINE_PER_BACKEND_KEYS_FOR_BACKEND, prefix) \
+          EndOf##fullname##Backends = prefix##Meta,
+
+  C10_FORALL_FUNCTIONALITY_KEYS(DEFINE_PER_BACKEND_KEYS)
+
+#undef DEFINE_PER_BACKEND_KEYS
+#undef DEFINE_PER_BACKEND_KEYS_FOR_BACKEND
+
+      EndOfRuntimeBackendKeys = EndOfAutogradFunctionalityBackends,
+
+  // ~~~~~~~~~~~~~~~~~~~~~~ Alias Dispatch Keys ~~~~~~~~~~~~~~~~~~~~~~~~~~ //
+  // Note [Alias Dispatch Keys]
+  // Alias dispatch keys are synthetic dispatch keys which map to multiple
+  // runtime dispatch keys. Alisa keys have precedence, but they are always
+  // lower precedence than runtime keys. You can register a kernel to an
+  // alias key, the kernel might be populated to the mapped runtime keys
+  // during dispatch table computation.
+  // If a runtime dispatch key has multiple kernels from alias keys, which
+  // kernel wins is done based on the precedence of alias keys (but runtime
+  // keys always have precedence over alias keys).
+  // Alias keys won't be directly called during runtime.
+
+  // See Note [Alias Dispatch Key : Autograd]
+  Autograd,
+  CompositeImplicitAutograd, // registered at
+  // build/aten/src/ATen/RegisterCompositeImplicitAutograd.cpp
+
+  // Note: The alias keyset for FuncTorchBatchedDecomposition is disjoint from
+  // all
+  // other alias keysets
+  // and so precedence order doesn't matter
+  FuncTorchBatchedDecomposition, // registered at
+  // build/aten/src/ATen/RegisterFuncTorchBatchedDecomposition.cpp
+  // Note: The alias keyset for CompositeImplicitAutogradNestedTensor is
+  // disjoint from all other alias keysets
+  CompositeImplicitAutogradNestedTensor, // registered at
+  // build/aten/src/ATen/RegisterCompositeImplicitAutogradNestedTensor.cpp
+  CompositeExplicitAutograd, // registered at
+  // build/aten/src/ATen/RegisterCompositeExplicitAutograd.cpp
+  // See Note [CompositeExplicitAutogradNonFunctional Key]
+  CompositeExplicitAutogradNonFunctional, // registered at
+  // build/aten/src/ATen/RegisterCompositeExplicitAutograd.cpp
+
+  // Define an alias key to represent end of alias dispatch keys.
+  // If you add new alias keys after Autograd, please also update it here.
+  StartOfAliasKeys = Autograd,
+  EndOfAliasKeys = CompositeExplicitAutogradNonFunctional, //
+
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~ BC ALIASES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ //
+  // The aliases exist for backwards compatibility reasons, they shouldn't
+  // be used
+  CPUTensorId = CPU,
+  CUDATensorId = CUDA,
+  DefaultBackend = CompositeExplicitAutograd,
+  PrivateUse1_PreAutograd = AutogradPrivateUse1,
+  PrivateUse2_PreAutograd = AutogradPrivateUse2,
+  PrivateUse3_PreAutograd = AutogradPrivateUse3,
+  Autocast = AutocastCUDA,
+};
+
+// Note [Private use DispatchKey]
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~
+// Private use tensor IDs are preallocated tensor type IDs for use in user
+// applications.  Similar to private use fields in HTTP, they can be used
+// by end users for experimental or private applications, without needing
+// to "standardize" the tensor ID (which would be done by submitting a PR
+// to PyTorch to add your type ID).
+//
+// Private use tensor IDs are appropriate to use if you want to experiment
+// with adding a new tensor type (without having to patch PyTorch first) or
+// have a private, non-distributed application that needs to make use of a
+// new tensor type.  Private use tensor IDs are NOT appropriate to use for
+// libraries intended to be distributed to further users: please contact
+// the PyTorch developers to get a type ID registered in this case.
+//
+// We provide two classes of private user tensor id: regular DispatchKeys
+// and Autograd DispatchKeys.  DispatchKeys serve the role of ordinary "backend"
+// DispatchKeys; if you were adding support for a new type of accelerator, you
+// would use a backend DispatchKey, and ideally automatically reuse
+// AutogradOther definitions already defined in PyTorch.  AutogradPrivateUse
+// DispatchKeys serve as "wrapper" DispatchKeys: they are only necessary for
+// tensors that compose multiple internal tensors, and for cases when the
+// built-in autograd formulas for operators are not appropriate.
+
+static_assert(
+    (static_cast<uint8_t>(BackendComponent::EndOfBackendKeys) +
+     static_cast<uint8_t>(DispatchKey::EndOfFunctionalityKeys)) <= 64,
+    "The BackendComponent and DispatchKey enums (below EndOfFunctionalityKeys)"
+    " both map to backend and functionality bits"
+    " into a 64-bit bitmask; you must have less than 64 total entries between them");
+
+// Check if a DispatchKey is an alias mapping to other runtime keys.
+constexpr bool isAliasDispatchKey(DispatchKey k) {
+  return k >= DispatchKey::StartOfAliasKeys && k <= DispatchKey::EndOfAliasKeys;
+}
+
+// [Note: Per-Backend Functionality Dispatch Keys]
+// Check if a DispatchKey is a per-backend functionality key
+// Any functionalities that can be customized per-backend should be added here.
+// These keys correspond to functionalities that can be customized individually
+// per backend. While they only take up one bit in the `DispatchKeySet` bitset,
+// they map to (# backends) slots in the operator table.
+// Each of these keys also has a separate set of "runtime keys" in the dispatch
+// key enum, per backend, which *do* map to the individual operator table slots.
+// For example, the "Sparse" key maps to an individual bit in the
+// DispatchKeySet, while `SparseCPU`, `SparseCUDA`, etc all map to individual
+// slots in the runtime operator table.
+
+constexpr bool isPerBackendFunctionalityKey(DispatchKey k) {
+  if (k == DispatchKey::Dense || k == DispatchKey::Quantized ||
+      k == DispatchKey::Sparse || k == DispatchKey::SparseCsr ||
+      k == DispatchKey::AutogradFunctionality ||
+      k == DispatchKey::NestedTensor) {
+    return true;
+  } else {
+    return false;
+  }
+}
+
+// Note that this includes Undefined in the total count.
+// BUT EndOfFunctionalityKeys is its own (placeholder) key.
+// e.g. Undefined=0, Dense=1, Sparse=2, EndOfFunctionalityKeys=3.
+// In the above example, there are 3 total functionality keys.
+constexpr uint8_t num_functionality_keys =
+    static_cast<uint8_t>(DispatchKey::EndOfFunctionalityKeys);
+
+constexpr uint8_t num_backends =
+    static_cast<uint8_t>(BackendComponent::EndOfBackendKeys);
+
+// Note [No More Than 16 Backends]
+// Search for this note to find places in the code where the "no more than 16
+// backends" invariant is baked in.
+static_assert(
+    static_cast<uint8_t>(BackendComponent::EndOfBackendKeys) <= 16,
+    "BackendComponent currently only supports <= 16 backends. If we really need to extend this, \
+there are a few places where this invariant is baked in");
+
+constexpr uint8_t numPerBackendFunctionalityKeys() {
+  uint8_t count = 0;
+  for (uint8_t k = 0; k <= num_functionality_keys; ++k) {
+    if (isPerBackendFunctionalityKey(static_cast<DispatchKey>(k)))
+      ++count;
+  }
+  return count;
+}
+
+#if defined(C10_MOBILE_TRIM_DISPATCH_KEYS)
+// See [Note: Trimmed Mobile Dispatch Keys]
+constexpr uint16_t num_runtime_entries = 8;
+#else
+constexpr uint16_t num_runtime_entries = num_functionality_keys +
+    (numPerBackendFunctionalityKeys() * (num_backends - 1));
+#endif
+
+// See Note [No More Than 16 Backends]
+constexpr uint16_t full_backend_mask =
+    (static_cast<uint16_t>(1) << num_backends) - 1;
+
+C10_API const char* toString(DispatchKey);
+C10_API const char* toString(BackendComponent);
+C10_API std::ostream& operator<<(std::ostream&, DispatchKey);
+C10_API std::ostream& operator<<(std::ostream&, BackendComponent);
+
+C10_API DispatchKey getAutogradKeyFromBackend(BackendComponent k);
+
+// Parses a string into a dispatch key.
+// If the string cannot be correctly parsed, throws an exception.
+C10_API c10::DispatchKey parseDispatchKey(const std::string& k);
+
+// These are some convenience identifiers for dispatch keys which are
+// shorter to type than their long counterparts.  Note that some of these
+// dispatch keys directly correspond to DeviceType; and most APIs that
+// accept DispatchKey also accept DeviceType; e.g.,
+// torch::dispatch(torch::kCPU, ...) is also valid.
+constexpr DispatchKey kAutograd = DispatchKey::Autograd;
+
+// See Note [The Ordering of Per-Backend Dispatch Keys Matters!]
+// This function relies on the invariant that the dispatch keys between
+// StartOfDenseBackends and EndOfRuntimeBackendKeys are ordered by backend
+// in the same order as `BackendComponent`.
+constexpr BackendComponent toBackendComponent(DispatchKey k) {
+  if (k >= DispatchKey::StartOfDenseBackends &&
+      k <= DispatchKey::EndOfDenseBackends) {
+    return static_cast<BackendComponent>(
+        static_cast<uint8_t>(k) -
+        static_cast<uint8_t>(DispatchKey::StartOfDenseBackends));
+  } else if (
+      k >= DispatchKey::StartOfQuantizedBackends &&
+      k <= DispatchKey::EndOfQuantizedBackends) {
+    return static_cast<BackendComponent>(
+        static_cast<uint8_t>(k) -
+        static_cast<uint8_t>(DispatchKey::StartOfQuantizedBackends));
+  } else if (
+      k >= DispatchKey::StartOfSparseBackends &&
+      k <= DispatchKey::EndOfSparseBackends) {
+    return static_cast<BackendComponent>(
+        static_cast<uint8_t>(k) -
+        static_cast<uint8_t>(DispatchKey::StartOfSparseBackends));
+  } else if (
+      k >= DispatchKey::StartOfSparseCsrBackends &&
+      k <= DispatchKey::EndOfSparseCsrBackends) {
+    return static_cast<BackendComponent>(
+        static_cast<uint8_t>(k) -
+        static_cast<uint8_t>(DispatchKey::StartOfSparseCsrBackends));
+  } else if (
+      k >= DispatchKey::StartOfNestedTensorBackends &&
+      k <= DispatchKey::EndOfNestedTensorBackends) {
+    return static_cast<BackendComponent>(
+        static_cast<uint8_t>(k) -
+        static_cast<uint8_t>(DispatchKey::StartOfNestedTensorBackends));
+  } else if (
+      k >= DispatchKey::StartOfAutogradFunctionalityBackends &&
+      k <= DispatchKey::EndOfAutogradFunctionalityBackends) {
+    return static_cast<BackendComponent>(
+        static_cast<uint8_t>(k) -
+        static_cast<uint8_t>(
+            DispatchKey::StartOfAutogradFunctionalityBackends));
+  } else {
+    return BackendComponent::InvalidBit;
+  }
+}
+
+constexpr DispatchKey toFunctionalityKey(DispatchKey k) {
+  if (k <= DispatchKey::EndOfFunctionalityKeys) {
+    return k;
+  } else if (k <= DispatchKey::EndOfDenseBackends) {
+    return DispatchKey::Dense;
+  } else if (k <= DispatchKey::EndOfQuantizedBackends) {
+    return DispatchKey::Quantized;
+  } else if (k <= DispatchKey::EndOfSparseBackends) {
+    return DispatchKey::Sparse;
+  } else if (k <= DispatchKey::EndOfSparseCsrBackends) {
+    return DispatchKey::SparseCsr;
+  } else if (k <= DispatchKey::EndOfNestedTensorBackends) {
+    return DispatchKey::NestedTensor;
+  } else if (k <= DispatchKey::EndOfAutogradFunctionalityBackends) {
+    return DispatchKey::AutogradFunctionality;
+  } else {
+    return DispatchKey::Undefined;
+  }
+}
+
+BackendComponent toBackendComponent(DeviceType device_type);
+
+// Given (DispatchKey::Dense, BackendComponent::CUDABit), returns
+// DispatchKey::CUDA.
+// See Note [The Ordering of Per-Backend Dispatch Keys Matters!]
+// This function relies on the invariant that the dispatch keys between
+// StartOfDenseBackends and EndOfRuntimeBackendKeys are ordered by backend
+// in the same order as `BackendComponent`.
+constexpr DispatchKey toRuntimePerBackendFunctionalityKey(
+    DispatchKey functionality_k,
+    BackendComponent backend_k) {
+  if (functionality_k == DispatchKey::Dense) {
+    return static_cast<DispatchKey>(
+        static_cast<uint8_t>(DispatchKey::StartOfDenseBackends) +
+        static_cast<uint8_t>(backend_k));
+  }
+  if (functionality_k == DispatchKey::Sparse) {
+    return static_cast<DispatchKey>(
+        static_cast<uint8_t>(DispatchKey::StartOfSparseBackends) +
+        static_cast<uint8_t>(backend_k));
+  }
+  if (functionality_k == DispatchKey::SparseCsr) {
+    return static_cast<DispatchKey>(
+        static_cast<uint8_t>(DispatchKey::StartOfSparseCsrBackends) +
+        static_cast<uint8_t>(backend_k));
+  }
+  if (functionality_k == DispatchKey::Quantized) {
+    return static_cast<DispatchKey>(
+        static_cast<uint8_t>(DispatchKey::StartOfQuantizedBackends) +
+        static_cast<uint8_t>(backend_k));
+  }
+  if (functionality_k == DispatchKey::NestedTensor) {
+    return static_cast<DispatchKey>(
+        static_cast<uint8_t>(DispatchKey::StartOfNestedTensorBackends) +
+        static_cast<uint8_t>(backend_k));
+  }
+  if (functionality_k == DispatchKey::AutogradFunctionality) {
+    return static_cast<DispatchKey>(
+        static_cast<uint8_t>(
+            DispatchKey::StartOfAutogradFunctionalityBackends) +
+        static_cast<uint8_t>(backend_k));
+  }
+  return DispatchKey::Undefined;
+}
+
+} // namespace c10
+
+namespace torch {
+// Expose the constant, but not the TYPE (DispatchKey is an implementation
+// detail!)
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using c10::kAutograd;
+} // namespace torch
+
+// NB: You really shouldn't use this instance; this enum is guaranteed
+// to be pretty small so a regular array should be acceptable.
+namespace std {
+template <>
+struct hash<c10::DispatchKey> {
+  typedef size_t result_type;
+  typedef c10::DispatchKey argument_type;
+
+  size_t operator()(c10::DispatchKey x) const {
+    return static_cast<size_t>(x);
+  }
+};
+} // namespace std

parrot/lib/python3.10/site-packages/torch/include/c10/core/DynamicCast.h

@@ -0,0 +1,125 @@
+#pragma once
+
+#include <c10/core/ScalarType.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Load.h>
+#include <c10/util/TypeCast.h>
+
+namespace c10 {
+
+// Dynamic type casting utils:
+// - fetch_and_cast
+// - cast_and_store
+//
+// fetch_and_cast fetch a value with dynamic type specified by a ScalarType
+// from a void pointer and cast it to a static type.
+//
+// cast_and_store casts a static typed value into dynamic type specified
+// by a ScalarType, and store it into a void pointer.
+//
+// NOTE:
+//
+// Dynamic casting allows us to support type promotion without blowing up
+// the combination space: For example, without dynamic cast, in order to
+// implement `add_` with type promotion, we would need something like
+//
+// AT_DISPATCH_ALL_TYPES(output.dtype(),
+//    AT_DISPATCH_ALL_TYPES(input1.dtype(),
+//       AT_DISPATCH_ALL_TYPES(input2.dtype(),
+//           [](arg0_t a, arg1_t b) -> out_t { return a + b; }
+//       )
+//    )
+// )
+//
+// If we support N dtypes, the above code would generate the a+b kernel for
+// all the N * N * N different supported types, the compilation time and
+// binary size would become horrible.
+//
+// Dynamic casting might sounds like a bad idea in terms of performance.
+// Especially if you ever do it in a loop, you are going to do a billion tests.
+// But in practice it is not as bad as it might look:
+//
+// - on CPU, this is a branch that always has the same outcome, therefore
+//   hopefully the branch predictor could do the job pretty well
+// - on GPU, these branches will not diverge, so we could still have the same
+//   warp executing the same line of code
+// - Most kernels, like `add`, are bandwidth bound, adding a few clock cycles to
+//   check an integer does not hurt the performance much because the ALUs would
+//   wait for load instructions anyway.
+//
+// For the discussion and benchmark, refer to:
+// - https://github.com/pytorch/pytorch/pull/28343
+// - https://github.com/pytorch/pytorch/pull/28344
+// - https://github.com/pytorch/pytorch/pull/28345
+//
+
+#ifdef C10_HOST_DEVICE
+#define ERROR_UNSUPPORTED_CAST CUDA_KERNEL_ASSERT(false);
+#else
+#define ERROR_UNSUPPORTED_CAST TORCH_CHECK(false, "Unexpected scalar type");
+#endif
+
+// Fetch a value with dynamic type src_type from ptr, and cast it to static type
+// dest_t.
+#define FETCH_AND_CAST_CASE(type, scalartype) \
+  case ScalarType::scalartype:                \
+    return c10::convert<dest_t>(c10::load<type>(ptr));
+
+template <typename dest_t>
+C10_HOST_DEVICE inline dest_t fetch_and_cast(
+    const ScalarType src_type,
+    const void* ptr) {
+  switch (src_type) {
+    AT_FORALL_SCALAR_TYPES_WITH_COMPLEX(FETCH_AND_CAST_CASE)
+    FETCH_AND_CAST_CASE(uint16_t, UInt16)
+    FETCH_AND_CAST_CASE(uint32_t, UInt32)
+    FETCH_AND_CAST_CASE(uint64_t, UInt64)
+    default:
+      ERROR_UNSUPPORTED_CAST
+  }
+  return dest_t(0); // just to avoid compiler warning
+}
+
+// Cast a value with static type src_t into dynamic dest_type, and store it to
+// ptr.
+#define CAST_AND_STORE_CASE(type, scalartype) \
+  case ScalarType::scalartype:                \
+    *(type*)ptr = c10::convert<type>(value);  \
+    return;
+template <typename src_t>
+C10_HOST_DEVICE inline void cast_and_store(
+    const ScalarType dest_type,
+    void* ptr,
+    src_t value) {
+  switch (dest_type) {
+    AT_FORALL_SCALAR_TYPES_WITH_COMPLEX(CAST_AND_STORE_CASE)
+    CAST_AND_STORE_CASE(uint16_t, UInt16)
+    CAST_AND_STORE_CASE(uint32_t, UInt32)
+    CAST_AND_STORE_CASE(uint64_t, UInt64)
+    default:;
+  }
+  ERROR_UNSUPPORTED_CAST
+}
+
+#define DEFINE_UNCASTABLE(T, scalartype_)                     \
+  template <>                                                 \
+  C10_HOST_DEVICE inline T fetch_and_cast<T>(                 \
+      const ScalarType src_type, const void* ptr) {           \
+    CUDA_KERNEL_ASSERT(ScalarType::scalartype_ == src_type);  \
+    return c10::load<T>(ptr);                                 \
+  }                                                           \
+  template <>                                                 \
+  C10_HOST_DEVICE inline void cast_and_store<T>(              \
+      const ScalarType dest_type, void* ptr, T value) {       \
+    CUDA_KERNEL_ASSERT(ScalarType::scalartype_ == dest_type); \
+    *(T*)ptr = value;                                         \
+  }
+
+AT_FORALL_QINT_TYPES(DEFINE_UNCASTABLE)
+
+#undef FETCH_AND_CAST_CASE
+#undef CAST_AND_STORE_CASE
+#undef DEFINE_UNCASTABLE
+#undef ERROR_UNSUPPORTED_CAST
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/GradMode.h

@@ -0,0 +1,44 @@
+#pragma once
+
+#include <c10/core/AutogradState.h>
+#include <c10/macros/Export.h>
+
+namespace c10 {
+
+struct C10_API GradMode {
+  static bool is_enabled();
+  static void set_enabled(bool enabled);
+};
+
+// A RAII, thread local (!) guard that enables or disables grad mode upon
+// construction, and sets it back to the original value upon destruction.
+struct C10_API AutoGradMode {
+  AutoGradMode(bool enabled) : prev_mode(GradMode::is_enabled()) {
+    GradMode::set_enabled(enabled);
+  }
+  ~AutoGradMode() {
+    GradMode::set_enabled(prev_mode);
+  }
+  bool prev_mode;
+};
+
+// A RAII, thread local (!) guard that stops future operations from building
+// gradients.
+struct C10_API NoGradGuard : public AutoGradMode {
+  NoGradGuard() : AutoGradMode(/*enabled=*/false) {}
+};
+
+// A RAII, thread local (!) guard that enables or disables forward grad mode
+// upon construction, and sets it back to the original value upon destruction.
+struct C10_API AutoFwGradMode {
+  AutoFwGradMode(bool enabled)
+      : prev_mode(AutogradState::get_tls_state().get_fw_grad_mode()) {
+    AutogradState::get_tls_state().set_fw_grad_mode(enabled);
+  }
+  ~AutoFwGradMode() {
+    AutogradState::get_tls_state().set_fw_grad_mode(prev_mode);
+  }
+  bool prev_mode;
+};
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/InferenceMode.h

@@ -0,0 +1,86 @@
+#pragma once
+
+#include <c10/core/AutogradState.h>
+#include <c10/core/DispatchKey.h>
+#include <c10/core/DispatchKeySet.h>
+#include <c10/core/impl/LocalDispatchKeySet.h>
+#include <c10/macros/Export.h>
+
+namespace c10 {
+
+// A RAII, thread local (!) guard that enables or disables inference mode upon
+// construction, and sets it back to the original value upon destruction.
+struct C10_API InferenceMode {
+  // Note [Expected TLS state in InferenceMode]:
+  //   InferenceMode: ADInplaceOrView not in
+  //   raw_local_dispatch_key_set.included(),
+  //                  Autograd in raw_local_dispatch_key_set.excluded()
+  //                  GradMode is disabled.
+  //   NormalMode: ADInplaceOrView in raw_local_dispatch_key_set.included(),
+  //               Autograd not in raw_local_dispatch_key_set.excluded()
+  //               GradMode is enabled by default unless toggled manually
+  //               through other APIs, e.g. NoGradGuard.
+  //
+  // Invariant:
+  // - ADInplaceOrView is never in the excluded set
+  // - Autograd is never in the included set
+  // - Setting InferenceMode will set GradMode accordingly, but not vice versa.
+  //
+  //  1. Why do we put ADInplaceOrView in included set outside InferenceMode?
+  //
+  //     Inplace update to inference tensor outside InferenceMode is not
+  //     allowed. See Note [Inplace update inference tensor] for more details.
+  //     Without going through ADInplaceOrView kernel, we cannot throw error
+  //     for `inference_tensor.add_(1)` case.
+  //
+  // 2. Why not put ADInplaceOrView in the excluded set inside InferenceMode?
+  //
+  //    For example:
+  //    torch::Tensor a = torch::ones({1, 2, 3}).set_requires_grad(true);
+  //    torch::Tensor k = a + 2;
+  //    {
+  //      c10::InferenceMode guard(true);
+  //      k.add_(2);
+  //    }
+  //    `k.add_(2)` still need to go through ADInplaceOrView kernel so that it's
+  //    prepared for future autograd.
+  //
+  // 3. Why does setting InferenceMode also set GradMode?
+  //
+  //    This is required since InferenceMode is a faster and more restrictive
+  //    version of NoGradGuard. All runtime checks using GradMode::is_enabled()
+  //    are applicable to InferenceMode as well, e.g.
+  //    `tensorTypeInCurrentExecutionContext` in interpreter.cpp.
+  InferenceMode(bool enabled = true)
+      : prev_mode(AutogradState::get_tls_state()),
+        prev_keyset(c10::impl::tls_local_dispatch_key_set()) {
+    // Enabling inference mode means disabling grad modes
+    // And disabling inference mode means enabling grad modes
+    AutogradState::set_tls_state(AutogradState(
+        /* grad_mode */ !enabled,
+        /* inference_mode */ enabled,
+        /* fw_grad_mode */ !enabled,
+        /* multithreading_enabled*/ !enabled));
+    DispatchKeySet included = enabled
+        ? prev_keyset.included_.remove(c10::DispatchKey::ADInplaceOrView)
+        : prev_keyset.included_.add(c10::DispatchKey::ADInplaceOrView);
+    DispatchKeySet excluded = enabled
+        ? (prev_keyset.excluded_ | c10::autograd_dispatch_keyset)
+        : (prev_keyset.excluded_ - c10::autograd_dispatch_keyset);
+    c10::impl::PODLocalDispatchKeySet cur_keyset{};
+    cur_keyset.set_included(included);
+    cur_keyset.set_excluded(excluded);
+    c10::impl::_force_tls_local_dispatch_key_set(cur_keyset);
+  }
+
+  ~InferenceMode() {
+    AutogradState::set_tls_state(prev_mode);
+    c10::impl::_force_tls_local_dispatch_key_set(prev_keyset);
+  }
+  static bool is_enabled();
+
+ private:
+  AutogradState prev_mode;
+  c10::impl::LocalDispatchKeySet prev_keyset;
+};
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/MemoryFormat.h

@@ -0,0 +1,290 @@
+#pragma once
+
+#include <c10/util/ArrayRef.h>
+#include <c10/util/Exception.h>
+
+#include <cstdint>
+#include <ostream>
+#include <vector>
+
+// Memory format is not the property of a Tensor. It is the way to tell an
+// operator how the result should be organized in memory and nothing more. That
+// means memory format should never be used as return value for any tensor state
+// interrogation functions (internally and externally).
+//
+// Possible options are:
+//  Preserve:
+//    If any of the input tensors is in channels_last format, operator output
+//    should be in channels_last format
+//
+//  Contiguous:
+//    Regardless of input tensors format, the output should be contiguous
+//    Tensor.
+//
+//  ChannelsLast:
+//    Regardless of input tensors format, the output should be in channels_last
+//    format.
+
+namespace c10 {
+enum class MemoryFormat : int8_t {
+  Contiguous,
+  Preserve,
+  ChannelsLast,
+  ChannelsLast3d,
+  NumOptions
+};
+
+// If you are seeing this, it means that this call site was not checked if
+// the memory format could be preserved, and it was switched to old default
+// behaviour of contiguous
+#define LEGACY_CONTIGUOUS_MEMORY_FORMAT c10::get_contiguous_memory_format()
+
+inline MemoryFormat get_contiguous_memory_format() {
+  return MemoryFormat::Contiguous;
+}
+
+inline std::ostream& operator<<(
+    std::ostream& stream,
+    at::MemoryFormat memory_format) {
+  switch (memory_format) {
+    case MemoryFormat::Preserve:
+      return stream << "Preserve";
+    case MemoryFormat::Contiguous:
+      return stream << "Contiguous";
+    case MemoryFormat::ChannelsLast:
+      return stream << "ChannelsLast";
+    case MemoryFormat::ChannelsLast3d:
+      return stream << "ChannelsLast3d";
+    default:
+      TORCH_CHECK(false, "Unknown memory format ", memory_format);
+  }
+}
+
+// Note: Hardcoded the channel last stride indices here to get better
+// performance
+template <typename T>
+inline std::vector<T> get_channels_last_strides_2d(ArrayRef<T> sizes) {
+  std::vector<T> strides(sizes.size());
+  switch (sizes.size()) {
+    case 4:
+      strides[1] = 1;
+      strides[3] = sizes[1];
+      strides[2] = strides[3] * sizes[3];
+      strides[0] = strides[2] * sizes[2];
+      return strides;
+    case 3:
+      strides[0] = 1;
+      strides[2] = sizes[0];
+      strides[1] = strides[2] * sizes[2];
+      return strides;
+    default:
+      TORCH_INTERNAL_ASSERT(
+          false, "ChannelsLast2d doesn't support size ", sizes.size());
+  }
+}
+
+inline std::vector<int64_t> get_channels_last_strides_2d(IntArrayRef sizes) {
+  return get_channels_last_strides_2d<int64_t>(sizes);
+}
+
+template <typename T>
+std::vector<T> get_channels_last_strides_3d(ArrayRef<T> sizes) {
+  std::vector<T> strides(sizes.size());
+  switch (sizes.size()) {
+    case 5:
+      strides[1] = 1;
+      strides[4] = sizes[1];
+      strides[3] = strides[4] * sizes[4];
+      strides[2] = strides[3] * sizes[3];
+      strides[0] = strides[2] * sizes[2];
+      return strides;
+    case 4:
+      strides[0] = 1;
+      strides[3] = sizes[0];
+      strides[2] = strides[3] * sizes[3];
+      strides[1] = strides[2] * sizes[2];
+      return strides;
+    default:
+      TORCH_INTERNAL_ASSERT(
+          false, "ChannelsLast3d doesn't support size ", sizes.size());
+  }
+}
+
+inline std::vector<int64_t> get_channels_last_strides_3d(IntArrayRef sizes) {
+  return get_channels_last_strides_3d<int64_t>(sizes);
+}
+
+// NOTE:
+// Below are Helper functions for is_channels_last_strides_xd.
+// 1. Please do not combine these helper functions, each helper function handles
+// exactly one case of sizes + memory_format, by doing this, the strides indices
+// will be a constant array and we can access it using constant index number,
+// the compiler will fully unroll the loop on strides indices to gain a better
+// performance.
+// 2. No error check in helper function, caller ensures the correctness of the
+// input
+// 3. All helper functions have similar comments, only 1st helper function is
+// commented here.
+template <typename T>
+inline bool is_channels_last_strides_2d_s4(
+    const ArrayRef<T> sizes,
+    const ArrayRef<T> strides) {
+  T min = 0;
+  // special case for trivial C dimension. default to NCHW
+  if (strides[1] == 0) {
+    return false;
+  }
+  // loop strides indices
+  for (auto& d : {1, 3, 2, 0}) {
+    if (sizes[d] == 0) {
+      return false;
+    }
+    if (strides[d] < min) {
+      return false;
+    }
+    // Fallback to NCHW as default layout for ambiguous cases
+    // This is the flaw of implicit memory_format from strides.
+    // N111 tensor with identical strides for size 1 dimension;
+    // Two cases could lead us here:
+    // a. N111 contiguous Tensor ([N,1,1,1]@[1,1,1,1])
+    // b. N11W contiguous Tensor sliced on the W-dimension.
+    // ([N,1,1,1]@[W,W,W,W])
+    if (d == 0 && min == strides[1]) {
+      return false;
+    }
+    // This is necessary to:
+    // 1. distinguish the memory_format of N1H1;
+    //     [H, 1, 1, 1] channels_last stride
+    //     [H, H, 1, 1] contiguous stride
+    // 2. permutation of 1C1W:
+    //     [1, C, 1, H]@[HC, H, H, 1] transpose(1, 3)
+    //     [1, H, 1, C]@[HC, 1, H, H] shouldn't be identified as channels_last
+    min = strides[d];
+    if (sizes[d] > 1) {
+      min *= sizes[d];
+    }
+  }
+  return true;
+}
+
+template <typename T>
+inline bool is_channels_last_strides_3d_s5(
+    const ArrayRef<T> sizes,
+    const ArrayRef<T> strides) {
+  T min = 0;
+  if (strides[1] == 0) {
+    return false;
+  }
+  for (auto& d : {1, 4, 3, 2, 0}) {
+    if (sizes[d] == 0) {
+      return false;
+    }
+    if (strides[d] < min) {
+      return false;
+    }
+    if (d == 0 && min == strides[1]) {
+      return false;
+    }
+    min = strides[d];
+    if (sizes[d] > 1) {
+      min *= sizes[d];
+    }
+  }
+  return true;
+}
+
+// Note [Ambiguous is_channels_last_strides_xd]
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+// The flaw of carrying memory_format implicitly through strides is very hard
+// to WAR properly. issue #24090
+// Without the history of permutation, we can't infer the memory_format of a
+// tensor from the snapshot of its size & stride
+// e.g.
+//
+// 1. We can NOT specify the memory_format of N111 tensor through strides in a
+//  meaningful way;
+//
+// 2. Two path that ended up with identical size/stride
+//  N11W contiguous tensor sliced at w-dimension becomes [N,1,1,1]@[W,W,W,W]
+//  NC11 channels_last tensor sliced at c-dimension becomes [N,1,1,1]@[C,C,C,C]
+//    So if we see a tensor [N,1,1,1]@[X,X,X,X], there's no way for us to infer
+//    the memory_format of the original tensor.
+//
+// Due to the limitations, our temporary WAR `is_channels_last_strides` does the
+// best effort to infer whether the original memory_format of a tensor is
+// at::MemoryFormat::ChannelsLast. The two objectives of this function (ordered
+// by their importance):
+//   1. Ensure that normal shape manipulation does not accidentally change the
+//      MemoryFormat of an existing tensor.
+//   2. Allows user to mark MemoryFormat::ChannelsLast to tensors;
+//
+// The function does so via checking strides of the tensor, including strides of
+// size-1 dimensions. Although conventionally PyTorch implies no restriction on
+// trivial stride (stride for size-1 dimension).
+//
+// Note that this approach is a compromise. We did not solve the problem
+// completely. Many cases we will not be able to infer the correct memory
+// format.
+// The implementation of `is_channels_last_strides` is to serve the objectives:
+// MemoryFormat::ChannelsLast has to be explicitly opted-in (no accidental
+// conversion); Best effort to maintain the ChannelsLast flag.
+//
+// Due to the fact that this is not a bulletproof solution, through testing
+// (aten/src/ATen/test/memory_format_test.cpp)
+//   a. we ensure that the common tasks are supported;
+//   a. we identify corner cases where the implementation compromises on.
+//
+// By the time accumulated permutation is enabled to replace implicit
+// memory_format through strides, we should be updating our tests and fix the
+// issues in our tests.
+//
+// We use Channels Last 2d as an example above.
+// This is a general problem for all the is_channels_last_strides_xd
+// implementation. Please check the helper functions
+// (is_channels_last_strides_*d_s*) for more details.
+
+template <typename T>
+inline bool is_channels_last_strides_2d(
+    const ArrayRef<T> sizes,
+    const ArrayRef<T> strides) {
+  switch (sizes.size()) {
+    case 4:
+      return is_channels_last_strides_2d_s4(sizes, strides);
+      // NOLINTNEXTLINE(bugprone-branch-clone)
+    case 3:
+      // TODO dim == 3 case will be enabled once it is fully tested
+      return false;
+    default:
+      return false;
+  }
+}
+
+template <typename T>
+inline bool is_channels_last_strides_3d(
+    const ArrayRef<T> sizes,
+    const ArrayRef<T> strides) {
+  switch (sizes.size()) {
+    case 5:
+      return is_channels_last_strides_3d_s5(sizes, strides);
+      // NOLINTNEXTLINE(bugprone-branch-clone)
+    case 4:
+      // TODO dim == 4 case will be enabled once it is fully tested
+      return false;
+    default:
+      return false;
+  }
+}
+
+inline bool is_channels_last_strides_2d(
+    const IntArrayRef sizes,
+    const IntArrayRef strides) {
+  return is_channels_last_strides_2d<int64_t>(sizes, strides);
+}
+
+inline bool is_channels_last_strides_3d(
+    const IntArrayRef sizes,
+    const IntArrayRef strides) {
+  return is_channels_last_strides_3d<int64_t>(sizes, strides);
+}
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/OptionalRef.h

@@ -0,0 +1,31 @@
+#pragma once
+
+namespace c10 {
+
+template <typename T>
+class OptionalRef {
+ public:
+  OptionalRef() : data_(nullptr) {}
+  OptionalRef(const T* data) : data_(data) {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(data_);
+  }
+  OptionalRef(const T& data) : data_(&data) {}
+
+  bool has_value() const {
+    return data_ != nullptr;
+  }
+
+  const T& get() const {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(data_);
+    return *data_;
+  }
+
+  operator bool() const {
+    return has_value();
+  }
+
+ private:
+  const T* data_;
+};
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/PyHandleCache.h

@@ -0,0 +1,76 @@
+#pragma once
+
+#include <c10/core/impl/PyInterpreter.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/python_stub.h>
+
+#include <atomic>
+
+namespace c10 {
+
+// A PyHandleCache represents a cached pointer from a C++ object to
+// a Python object that represents that object analogously in Python.
+// Upon a cache hit, the relevant object can be retrieved after a test
+// and then a memory load.  Two conditions must hold to be able to use this
+// class:
+//
+//  - This must truly be a cache; e.g., the caller must be able to produce
+//    the object some other way if the cache hit misses.
+//
+//  - This must truly be a handle; e.g., the Python object referenced by
+//    this class must have static lifetime.  This means we don't have to
+//    maintain strong ownership or deallocate the object when the C++ object
+//    dies.  Static lifetime is a good idea in conjunction with the cache,
+//    since if you are producing a fresh object on miss you won't be
+//    maintaining object identity.  If you need bidirectional ownership,
+//    you will want to factor out the pattern in TensorImpl with
+//    resurrection.
+//
+// This cache is expected to not improve perf under torchdeploy, as one
+// interpreter will fill up the cache, and all the interpreters will be
+// unable to use the slot.  A potential improvement is to have multiple
+// slots (one per interpreter), which will work in deployment scenarios
+// where there a stable, fixed number of interpreters.  You can also store
+// the relevant state in the Python library, rather than in the non-Python
+// library (although in many cases, this is not convenient, as there may
+// not be a way to conveniently index based on the object.)
+class PyHandleCache {
+ public:
+  PyHandleCache() : pyinterpreter_(nullptr) {}
+
+  // Attempt to fetch the pointer from the cache, if the PyInterpreter
+  // matches.  If it doesn't exist, or the cache entry is not valid,
+  // use slow_accessor to get the real pointer value and return that
+  // (possibly writing it to the cache, if the cache entry is
+  // available.)
+  template <typename F>
+  PyObject* ptr_or(impl::PyInterpreter* self_interpreter, F slow_accessor)
+      const {
+    // Note [Memory ordering on Python interpreter tag]
+    impl::PyInterpreter* interpreter =
+        pyinterpreter_.load(std::memory_order_acquire);
+    if (C10_LIKELY(interpreter == self_interpreter)) {
+      return data_;
+    } else if (interpreter == nullptr) {
+      auto* r = slow_accessor();
+      impl::PyInterpreter* expected = nullptr;
+      // attempt to claim this cache entry with the specified interpreter tag
+      if (pyinterpreter_.compare_exchange_strong(
+              expected, self_interpreter, std::memory_order_acq_rel)) {
+        data_ = r;
+      }
+      // This shouldn't be possible, as you should be GIL protected
+      TORCH_INTERNAL_ASSERT(expected != self_interpreter);
+      return r;
+    } else {
+      return slow_accessor();
+    }
+  }
+
+ private:
+  mutable std::atomic<impl::PyInterpreter*> pyinterpreter_;
+  mutable PyObject* data_{nullptr};
+};
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/QScheme.h

@@ -0,0 +1,50 @@
+#pragma once
+
+#include <c10/util/Exception.h>
+#include <cstdint>
+#include <string>
+
+namespace c10 {
+
+/**
+ * QScheme is an enum that specifies the type of quantization. This has a one
+ * to one correspondence with Quantizer
+ * Please refer to ATen/quantized/Quantizer.h to see the Quantizers classes.
+ * Keep this file in sync with torch/nn/_qscheme.py
+ */
+enum class QScheme : uint8_t {
+  PER_TENSOR_AFFINE = 0,
+  PER_CHANNEL_AFFINE = 1,
+  PER_TENSOR_SYMMETRIC = 2,
+  PER_CHANNEL_SYMMETRIC = 3,
+  PER_CHANNEL_AFFINE_FLOAT_QPARAMS = 4,
+  COMPILE_TIME_NUM_QSCHEMES = 5,
+};
+
+constexpr auto kPerTensorAffine = QScheme::PER_TENSOR_AFFINE;
+constexpr auto kPerChannelAffine = QScheme::PER_CHANNEL_AFFINE;
+constexpr auto kPerTensorSymmetric = QScheme::PER_TENSOR_SYMMETRIC;
+constexpr auto kPerChannelSymmetric = QScheme::PER_CHANNEL_SYMMETRIC;
+constexpr auto kPerChannelAffineFloatQParams =
+    QScheme::PER_CHANNEL_AFFINE_FLOAT_QPARAMS;
+constexpr int COMPILE_TIME_NUM_QSCHEMES =
+    static_cast<int>(QScheme::COMPILE_TIME_NUM_QSCHEMES);
+
+inline std::string toString(QScheme qscheme) {
+  switch (qscheme) {
+    case kPerTensorAffine:
+      return "per_tensor_affine";
+    case kPerChannelAffine:
+      return "per_channel_affine";
+    case kPerTensorSymmetric:
+      return "per_tensor_symmetric";
+    case kPerChannelSymmetric:
+      return "per_channel_symmetric";
+    case kPerChannelAffineFloatQParams:
+      return "per_channel_affine_float_qparams";
+    default:
+      TORCH_CHECK(false, "Unrecognized qscheme: ", static_cast<int>(qscheme));
+  }
+}
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/RefcountedDeleter.h

@@ -0,0 +1,52 @@
+#pragma once
+
+#include <c10/core/Storage.h>
+#include <c10/macros/Export.h>
+#include <c10/util/UniqueVoidPtr.h>
+
+#include <atomic>
+#include <memory>
+
+namespace c10 {
+
+// A RefcountedDeleterContext object is used as the `ctx` argument for DataPtr
+// to implement a shared DataPtr. Normally, a DataPtr is unique, but we use
+// this custom context and the `refcounted_deleter` function below to make the
+// DataPtr act like a non-unique DataPtr. This context object holds onto an
+// inner context and deleter function which handle the actual deletion of the
+// data when the refcount reaches 0.
+//
+// This shared DataPtr feature is only used when storages are shared between
+// multiple Python interpreters in MultiPy. Before storages had PyObject
+// preservation, interpreters could just share the same StorageImpl instance.
+// But now a StorageImpl can only be associated with one interpreter in order
+// to properly manage a zombie PyObject. So we share storages across Python
+// interpreters by creating a different StorageImpl instance for each one, but
+// they all point to the same data.
+struct C10_API RefcountedDeleterContext {
+  RefcountedDeleterContext(void* other_ctx, c10::DeleterFnPtr other_deleter)
+      : other_ctx(other_ctx, other_deleter), refcount(1) {}
+
+  std::unique_ptr<void, c10::DeleterFnPtr> other_ctx;
+  std::atomic_int refcount;
+};
+
+// `refcounted_deleter` is used as the `ctx_deleter` for DataPtr to implement
+// a shared DataPtr.
+//
+// Warning: This should only be called on a pointer to
+// a RefcountedDeleterContext that was allocated on the heap with `new`,
+// because when the refcount reaches 0, the context is deleted with `delete`
+C10_API void refcounted_deleter(void* ctx_);
+
+// If the storage's DataPtr does not use `refcounted_deleter`, replace it with
+// a DataPtr that does, so it can be shared between multiple StorageImpls
+C10_API void maybeApplyRefcountedDeleter(const c10::Storage& storage);
+
+// Create a new StorageImpl that points to the same data. If the original
+// StorageImpl's DataPtr does not use `refcounted_deleter`, it will be replaced
+// with one that does
+C10_API c10::Storage newStorageImplFromRefcountedDataPtr(
+    const c10::Storage& storage);
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/SafePyObject.h

@@ -0,0 +1,99 @@
+#pragma once
+
+#include <c10/core/impl/PyInterpreter.h>
+#include <c10/macros/Export.h>
+#include <c10/util/python_stub.h>
+#include <utility>
+
+namespace c10 {
+
+// This is an safe owning holder for a PyObject, akin to pybind11's
+// py::object, with two major differences:
+//
+//  - It is in c10/core; i.e., you can use this type in contexts where
+//    you do not have a libpython dependency
+//
+//  - It is multi-interpreter safe (ala torchdeploy); when you fetch
+//    the underlying PyObject* you are required to specify what the current
+//    interpreter context is and we will check that you match it.
+//
+// It is INVALID to store a reference to a Tensor object in this way;
+// you should just use TensorImpl directly in that case!
+struct C10_API SafePyObject {
+  // Steals a reference to data
+  SafePyObject(PyObject* data, c10::impl::PyInterpreter* pyinterpreter)
+      : data_(data), pyinterpreter_(pyinterpreter) {}
+  SafePyObject(SafePyObject&& other) noexcept
+      : data_(std::exchange(other.data_, nullptr)),
+        pyinterpreter_(other.pyinterpreter_) {}
+
+  // In principle this could be copyable if we add an incref to PyInterpreter
+  // but for now it's easier to just disallow it.
+  SafePyObject(SafePyObject const&) = delete;
+  SafePyObject& operator=(SafePyObject const&) = delete;
+
+  ~SafePyObject() {
+    if (data_ != nullptr) {
+      (*pyinterpreter_)->decref(data_, /*has_pyobj_slot*/ false);
+    }
+  }
+
+  c10::impl::PyInterpreter& pyinterpreter() const {
+    return *pyinterpreter_;
+  }
+  PyObject* ptr(const c10::impl::PyInterpreter*) const;
+
+  // stop tracking the current object, and return it
+  PyObject* release() {
+    auto rv = data_;
+    data_ = nullptr;
+    return rv;
+  }
+
+ private:
+  PyObject* data_;
+  c10::impl::PyInterpreter* pyinterpreter_;
+};
+
+// A newtype wrapper around SafePyObject for type safety when a python object
+// represents a specific type. Note that `T` is only used as a tag and isn't
+// actually used for any true purpose.
+template <typename T>
+struct SafePyObjectT : private SafePyObject {
+  SafePyObjectT(PyObject* data, c10::impl::PyInterpreter* pyinterpreter)
+      : SafePyObject(data, pyinterpreter) {}
+  SafePyObjectT(SafePyObjectT&& other) noexcept : SafePyObject(other) {}
+  SafePyObjectT(SafePyObjectT const&) = delete;
+  SafePyObjectT& operator=(SafePyObjectT const&) = delete;
+
+  using SafePyObject::ptr;
+  using SafePyObject::pyinterpreter;
+  using SafePyObject::release;
+};
+
+// Like SafePyObject, but non-owning.  Good for references to global PyObjects
+// that will be leaked on interpreter exit.  You get a copy constructor/assign
+// this way.
+struct C10_API SafePyHandle {
+  SafePyHandle() : data_(nullptr), pyinterpreter_(nullptr) {}
+  SafePyHandle(PyObject* data, c10::impl::PyInterpreter* pyinterpreter)
+      : data_(data), pyinterpreter_(pyinterpreter) {}
+
+  c10::impl::PyInterpreter& pyinterpreter() const {
+    return *pyinterpreter_;
+  }
+  PyObject* ptr(const c10::impl::PyInterpreter*) const;
+  void reset() {
+    data_ = nullptr;
+    pyinterpreter_ = nullptr;
+  }
+  operator bool() {
+    return data_;
+  }
+
+ private:
+  PyObject* data_;
+  c10::impl::PyInterpreter* pyinterpreter_;
+};
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/ScalarType.h

@@ -0,0 +1,564 @@
+#pragma once
+
+#include <c10/util/BFloat16.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Float8_e4m3fn.h>
+#include <c10/util/Float8_e4m3fnuz.h>
+#include <c10/util/Float8_e5m2.h>
+#include <c10/util/Float8_e5m2fnuz.h>
+#include <c10/util/Half.h>
+#include <c10/util/bits.h>
+#include <c10/util/complex.h>
+#include <c10/util/qint32.h>
+#include <c10/util/qint8.h>
+#include <c10/util/quint2x4.h>
+#include <c10/util/quint4x2.h>
+#include <c10/util/quint8.h>
+
+#include <array>
+#include <cstddef>
+#include <cstdint>
+#include <limits>
+#include <ostream>
+#include <type_traits>
+
+namespace c10 {
+
+// dummy struct for uint1 to uint7, actual functionality
+// of these dtypes will be implemented in python with Tensor subclass
+template <unsigned int N>
+struct dummy_uint1_7_t {};
+
+// For the macros below:
+//
+// For users: If you want to macro some code for all non-QInt scalar types
+// (i.e. types with complete information, you probably want one of the
+// AT_FORALL_SCALAR_TYPES / AT_FORALL_SCALAR_TYPES_AND macros below, which are
+// designed to behave similarly to the Dispatch macros with the same name.
+//
+// For adding a new dtype: In the beginning, we had an idea that there was a
+// list of all scalar types, and you could use AT_FORALL_SCALAR_TYPES to
+// iterate over them.  But over the years we added weird types which couldn't
+// be handled uniformly everywhere and so in the end we ended up with some
+// mish-mosh of some helper macros, but mostly use sites making a call about
+// what dtypes they can or can't support.  So if you want to add a new dtype,
+// the preferred resolution is to find a dtype similar to what you want,
+// grep for it and edit all the sites you find this way.  If you need to add
+// a completely new kind of dtype, you're going to have to laboriously audit
+// all of the sites everywhere to figure out how it should work.  Consulting
+// some old PRs where we added new dtypes (check history of this file) can
+// help give you an idea where to start.
+
+// NB: Order matters for this macro; it is relied upon in
+// _promoteTypesLookup and the serialization format.
+#define AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(_) \
+  _(uint8_t, Byte) /* 0 */                               \
+  _(int8_t, Char) /* 1 */                                \
+  _(int16_t, Short) /* 2 */                              \
+  _(int, Int) /* 3 */                                    \
+  _(int64_t, Long) /* 4 */                               \
+  _(at::Half, Half) /* 5 */                              \
+  _(float, Float) /* 6 */                                \
+  _(double, Double) /* 7 */                              \
+  _(c10::complex<c10::Half>, ComplexHalf) /* 8 */        \
+  _(c10::complex<float>, ComplexFloat) /* 9 */           \
+  _(c10::complex<double>, ComplexDouble) /* 10 */        \
+  _(bool, Bool) /* 11 */                                 \
+  _(c10::qint8, QInt8) /* 12 */                          \
+  _(c10::quint8, QUInt8) /* 13 */                        \
+  _(c10::qint32, QInt32) /* 14 */                        \
+  _(at::BFloat16, BFloat16) /* 15 */                     \
+  _(c10::quint4x2, QUInt4x2) /* 16 */                    \
+  _(c10::quint2x4, QUInt2x4) /* 17 */                    \
+  _(c10::bits1x8, Bits1x8) /* 18 */                      \
+  _(c10::bits2x4, Bits2x4) /* 19 */                      \
+  _(c10::bits4x2, Bits4x2) /* 20 */                      \
+  _(c10::bits8, Bits8) /* 21 */                          \
+  _(c10::bits16, Bits16) /* 22 */                        \
+  _(c10::Float8_e5m2, Float8_e5m2) /* 23 */              \
+  _(c10::Float8_e4m3fn, Float8_e4m3fn) /* 24 */          \
+  _(c10::Float8_e5m2fnuz, Float8_e5m2fnuz) /* 25 */      \
+  _(c10::Float8_e4m3fnuz, Float8_e4m3fnuz) /* 26 */      \
+  _(uint16_t, UInt16) /* 27 */                           \
+  _(uint32_t, UInt32) /* 28 */                           \
+  _(uint64_t, UInt64) /* 29 */                           \
+  _(c10::dummy_uint1_7_t<1>, UInt1) /* 30 */             \
+  _(c10::dummy_uint1_7_t<2>, UInt2) /* 31 */             \
+  _(c10::dummy_uint1_7_t<3>, UInt3) /* 32 */             \
+  _(c10::dummy_uint1_7_t<4>, UInt4) /* 33 */             \
+  _(c10::dummy_uint1_7_t<5>, UInt5) /* 34 */             \
+  _(c10::dummy_uint1_7_t<6>, UInt6) /* 35 */             \
+  _(c10::dummy_uint1_7_t<7>, UInt7) /* 36 */
+
+// If you want to support ComplexHalf for real, add ComplexHalf
+// into this macro (and change the name).  But beware: convert()
+// doesn't work for all the conversions you need...
+//
+// TODO: To add unsigned int types here, we must define accumulate type.
+// But uint8 currently accumulates into int64, so we would have to make
+// an inconsistent choice for the larger types.  Difficult.
+#define AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_EXCEPT_COMPLEX_HALF_F8NZ(_) \
+  _(uint8_t, Byte)                                                      \
+  _(int8_t, Char)                                                       \
+  _(int16_t, Short)                                                     \
+  _(int, Int)                                                           \
+  _(int64_t, Long)                                                      \
+  _(at::Half, Half)                                                     \
+  _(float, Float)                                                       \
+  _(double, Double)                                                     \
+  _(c10::complex<float>, ComplexFloat)                                  \
+  _(c10::complex<double>, ComplexDouble)                                \
+  _(bool, Bool)                                                         \
+  _(at::BFloat16, BFloat16)                                             \
+  _(at::Float8_e5m2, Float8_e5m2)                                       \
+  _(at::Float8_e4m3fn, Float8_e4m3fn)
+
+// This macro controls many of our C++ APIs, including constructors
+// for Scalar as well as the data() and item() accessors on Tensor
+#define AT_FORALL_SCALAR_TYPES_WITH_COMPLEX(_) \
+  _(uint8_t, Byte)                             \
+  _(int8_t, Char)                              \
+  _(int16_t, Short)                            \
+  _(int, Int)                                  \
+  _(int64_t, Long)                             \
+  _(at::Half, Half)                            \
+  _(float, Float)                              \
+  _(double, Double)                            \
+  _(c10::complex<c10::Half>, ComplexHalf)      \
+  _(c10::complex<float>, ComplexFloat)         \
+  _(c10::complex<double>, ComplexDouble)       \
+  _(bool, Bool)                                \
+  _(at::BFloat16, BFloat16)                    \
+  _(at::Float8_e5m2, Float8_e5m2)              \
+  _(at::Float8_e4m3fn, Float8_e4m3fn)          \
+  _(at::Float8_e5m2fnuz, Float8_e5m2fnuz)      \
+  _(at::Float8_e4m3fnuz, Float8_e4m3fnuz)
+
+enum class ScalarType : int8_t {
+#define DEFINE_ST_ENUM_VAL_(_1, n) n,
+  AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_ST_ENUM_VAL_)
+#undef DEFINE_ENUM_ST_ENUM_VAL_
+      Undefined,
+  NumOptions
+};
+
+constexpr uint16_t NumScalarTypes =
+    static_cast<uint16_t>(ScalarType::NumOptions);
+
+namespace impl {
+
+// These are used to map ScalarTypes to C++ types.
+
+template <c10::ScalarType N>
+struct ScalarTypeToCPPType;
+
+#define SPECIALIZE_ScalarTypeToCPPType(cpp_type, scalar_type)                \
+  template <>                                                                \
+  struct ScalarTypeToCPPType<c10::ScalarType::scalar_type> {                 \
+    using type = cpp_type;                                                   \
+                                                                             \
+    /* This is a workaround for the CUDA bug which prevents */               \
+    /* ::detail::ScalarTypeToCType<T>::type being used directly due to */    \
+    /* ambiguous reference which can't to be resolved. For some reason it */ \
+    /* can't pick between at::detail and at::cuda::detail. */                \
+    /* For repro example, please see: */                                     \
+    /* https://gist.github.com/izdeby/952ae7cf256ddb740a73776d39a7e7ba */    \
+    /* TODO: remove once the bug is fixed. */                                \
+    static type t;                                                           \
+  };
+
+AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(SPECIALIZE_ScalarTypeToCPPType)
+
+#undef SPECIALIZE_ScalarTypeToCPPType
+
+template <c10::ScalarType N>
+using ScalarTypeToCPPTypeT = typename ScalarTypeToCPPType<N>::type;
+
+} // namespace impl
+
+template <typename T>
+struct CppTypeToScalarType;
+
+#define SPECIALIZE_CppTypeToScalarType(cpp_type, scalar_type)                  \
+  template <>                                                                  \
+  struct CppTypeToScalarType<cpp_type>                                         \
+      : std::                                                                  \
+            integral_constant<c10::ScalarType, c10::ScalarType::scalar_type> { \
+  };
+
+AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(SPECIALIZE_CppTypeToScalarType)
+
+#undef SPECIALIZE_CppTypeToScalarType
+
+// NB: despite its generic sounding name, the macros that don't take _AND
+// are mostly only used by tensorexpr
+#define AT_FORALL_INT_TYPES(_) \
+  _(uint8_t, Byte)             \
+  _(int8_t, Char)              \
+  _(int16_t, Short)            \
+  _(int, Int)                  \
+  _(int64_t, Long)
+
+#define AT_FORALL_SCALAR_TYPES(_) \
+  _(uint8_t, Byte)                \
+  _(int8_t, Char)                 \
+  _(int16_t, Short)               \
+  _(int, Int)                     \
+  _(int64_t, Long)                \
+  _(float, Float)                 \
+  _(double, Double)
+
+// These macros are often controlling how many template instantiations we
+// create for kernels.  It is typically inappropriate to add new dtypes here,
+// instead, new types should be added to use sites on a case-by-case basis.
+// We generally are not accepting new dtypes due to binary size concerns.
+
+#define AT_FORALL_SCALAR_TYPES_AND(SCALARTYPE, _) \
+  _(uint8_t, Byte)                                \
+  _(int8_t, Char)                                 \
+  _(int16_t, Short)                               \
+  _(int, Int)                                     \
+  _(int64_t, Long)                                \
+  _(float, Float)                                 \
+  _(double, Double)                               \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE>::t),  \
+    SCALARTYPE)
+
+#define AT_FORALL_SCALAR_TYPES_AND2(SCALARTYPE1, SCALARTYPE2, _) \
+  _(uint8_t, Byte)                                               \
+  _(int8_t, Char)                                                \
+  _(int16_t, Short)                                              \
+  _(int, Int)                                                    \
+  _(int64_t, Long)                                               \
+  _(float, Float)                                                \
+  _(double, Double)                                              \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<                   \
+             ::c10::ScalarType::SCALARTYPE1>::t),                \
+    SCALARTYPE1)                                                 \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<                   \
+             ::c10::ScalarType::SCALARTYPE2>::t),                \
+    SCALARTYPE2)
+
+#define AT_FORALL_SCALAR_TYPES_AND3(SCALARTYPE1, SCALARTYPE2, SCALARTYPE3, _) \
+  _(uint8_t, Byte)                                                            \
+  _(int8_t, Char)                                                             \
+  _(int16_t, Short)                                                           \
+  _(int, Int)                                                                 \
+  _(int64_t, Long)                                                            \
+  _(float, Float)                                                             \
+  _(double, Double)                                                           \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<                                \
+             ::c10::ScalarType::SCALARTYPE1>::t),                             \
+    SCALARTYPE1)                                                              \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<                                \
+             ::c10::ScalarType::SCALARTYPE2>::t),                             \
+    SCALARTYPE2)                                                              \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<                                \
+             ::c10::ScalarType::SCALARTYPE3>::t),                             \
+    SCALARTYPE3)
+
+#define AT_FORALL_SCALAR_TYPES_AND7(              \
+    SCALARTYPE1,                                  \
+    SCALARTYPE2,                                  \
+    SCALARTYPE3,                                  \
+    SCALARTYPE4,                                  \
+    SCALARTYPE5,                                  \
+    SCALARTYPE6,                                  \
+    SCALARTYPE7,                                  \
+    _)                                            \
+  _(uint8_t, Byte)                                \
+  _(int8_t, Char)                                 \
+  _(int16_t, Short)                               \
+  _(int, Int)                                     \
+  _(int64_t, Long)                                \
+  _(float, Float)                                 \
+  _(double, Double)                               \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE1>::t), \
+    SCALARTYPE1)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE2>::t), \
+    SCALARTYPE2)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE3>::t), \
+    SCALARTYPE3)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE4>::t), \
+    SCALARTYPE4)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE5>::t), \
+    SCALARTYPE5)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE6>::t), \
+    SCALARTYPE6)                                  \
+  _(decltype(::c10::impl::ScalarTypeToCPPType<    \
+             ::c10::ScalarType::SCALARTYPE7>::t), \
+    SCALARTYPE7)
+
+#define AT_FORALL_QINT_TYPES(_) \
+  _(c10::qint8, QInt8)          \
+  _(c10::quint8, QUInt8)        \
+  _(c10::qint32, QInt32)        \
+  _(c10::quint4x2, QUInt4x2)    \
+  _(c10::quint2x4, QUInt2x4)
+
+#define AT_FORALL_COMPLEX_TYPES(_)     \
+  _(c10::complex<float>, ComplexFloat) \
+  _(c10::complex<double>, ComplexDouble)
+
+#define DEFINE_CONSTANT(_, name) \
+  constexpr ScalarType k##name = ScalarType::name;
+
+// NOLINTNEXTLINE(clang-diagnostic-unused-const-variable)
+AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_CONSTANT)
+#undef DEFINE_CONSTANT
+
+inline const char* toString(ScalarType t) {
+#define DEFINE_CASE(_, name) \
+  case ScalarType::name:     \
+    return #name;
+
+  switch (t) {
+    AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(DEFINE_CASE)
+    default:
+      return "UNKNOWN_SCALAR";
+  }
+#undef DEFINE_CASE
+}
+
+inline size_t elementSize(ScalarType t) {
+#define CASE_ELEMENTSIZE_CASE(ctype, name) \
+  case ScalarType::name:                   \
+    return sizeof(ctype);
+
+  switch (t) {
+    AT_FORALL_SCALAR_TYPES_WITH_COMPLEX_AND_QINTS(CASE_ELEMENTSIZE_CASE)
+    default:
+      TORCH_CHECK(false, "Unknown ScalarType");
+  }
+#undef CASE_ELEMENTSIZE_CASE
+}
+
+inline bool isIntegralType(ScalarType t, bool includeBool) {
+  bool isIntegral =
+      (t == ScalarType::Byte || t == ScalarType::Char || t == ScalarType::Int ||
+       t == ScalarType::Long || t == ScalarType::Short ||
+       t == ScalarType::UInt16 || t == ScalarType::UInt32 ||
+       t == ScalarType::UInt64);
+
+  return isIntegral || (includeBool && t == ScalarType::Bool);
+}
+
+C10_DEPRECATED_MESSAGE(
+    "isIntegralType is deprecated. Please use the overload with 'includeBool' parameter instead.")
+inline bool isIntegralType(ScalarType t) {
+  return isIntegralType(t, /*includeBool=*/false);
+}
+
+inline bool isFloat8Type(ScalarType t) {
+  return t == ScalarType::Float8_e5m2 || t == ScalarType::Float8_e5m2fnuz ||
+      t == ScalarType::Float8_e4m3fn || t == ScalarType::Float8_e4m3fnuz;
+}
+
+inline bool isReducedFloatingType(ScalarType t) {
+  return t == ScalarType::Half || t == ScalarType::BFloat16 || isFloat8Type(t);
+}
+
+inline bool isFloatingType(ScalarType t) {
+  return t == ScalarType::Double || t == ScalarType::Float ||
+      isReducedFloatingType(t);
+}
+
+inline bool isComplexType(ScalarType t) {
+  return (
+      t == ScalarType::ComplexHalf || t == ScalarType::ComplexFloat ||
+      t == ScalarType::ComplexDouble);
+}
+
+inline bool isQIntType(ScalarType t) {
+  // Don't forget to extend this when adding new QInt types
+  return t == ScalarType::QInt8 || t == ScalarType::QUInt8 ||
+      t == ScalarType::QInt32 || t == ScalarType::QUInt4x2 ||
+      t == ScalarType::QUInt2x4;
+}
+
+inline bool isBitsType(ScalarType t) {
+  return t == ScalarType::Bits1x8 || t == ScalarType::Bits2x4 ||
+      t == ScalarType::Bits4x2 || t == ScalarType::Bits8 ||
+      t == ScalarType::Bits16;
+}
+
+inline bool isBarebonesUnsignedType(ScalarType t) {
+  return t == ScalarType::UInt1 || t == ScalarType::UInt2 ||
+      t == ScalarType::UInt3 || t == ScalarType::UInt4 ||
+      t == ScalarType::UInt5 || t == ScalarType::UInt6 ||
+      t == ScalarType::UInt7 || t == ScalarType::UInt16 ||
+      t == ScalarType::UInt32 || t == ScalarType::UInt64;
+}
+
+inline ScalarType toQIntType(ScalarType t) {
+  switch (t) {
+    case ScalarType::Byte:
+      return ScalarType::QUInt8;
+    case ScalarType::Char:
+      return ScalarType::QInt8;
+    case ScalarType::Int:
+      return ScalarType::QInt32;
+    default:
+      return t;
+  }
+}
+
+inline ScalarType toUnderlying(ScalarType t) {
+  switch (t) {
+    case ScalarType::QUInt8:
+    case ScalarType::QUInt4x2:
+      [[fallthrough]];
+    case ScalarType::QUInt2x4:
+      return ScalarType::Byte;
+    case ScalarType::QInt8:
+      return ScalarType::Char;
+    case ScalarType::QInt32:
+      return ScalarType::Int;
+    default:
+      return t;
+  }
+}
+
+inline bool isSignedType(ScalarType t) {
+#define CASE_ISSIGNED(name)     \
+  case ScalarType::name:        \
+    return std::numeric_limits< \
+        ::c10::impl::ScalarTypeToCPPTypeT<ScalarType::name>>::is_signed;
+
+  switch (t) {
+    case ScalarType::QInt8:
+    case ScalarType::QUInt8:
+    case ScalarType::QInt32:
+    case ScalarType::QUInt4x2:
+    case ScalarType::QUInt2x4:
+      TORCH_CHECK(false, "isSignedType not supported for quantized types");
+    case ScalarType::Bits1x8:
+    case ScalarType::Bits2x4:
+    case ScalarType::Bits4x2:
+    case ScalarType::Bits8:
+    case ScalarType::Bits16:
+      TORCH_CHECK(false, "Bits types are undefined");
+      CASE_ISSIGNED(UInt16);
+      CASE_ISSIGNED(UInt32);
+      CASE_ISSIGNED(UInt64);
+      CASE_ISSIGNED(BFloat16);
+      CASE_ISSIGNED(Float8_e5m2);
+      CASE_ISSIGNED(Float8_e5m2fnuz);
+      CASE_ISSIGNED(Float8_e4m3fn);
+      CASE_ISSIGNED(Float8_e4m3fnuz);
+      CASE_ISSIGNED(Byte);
+      CASE_ISSIGNED(Char);
+      CASE_ISSIGNED(Short);
+      CASE_ISSIGNED(Int);
+      CASE_ISSIGNED(Long);
+      CASE_ISSIGNED(Half);
+      CASE_ISSIGNED(Float);
+      CASE_ISSIGNED(Double);
+      CASE_ISSIGNED(ComplexHalf);
+      CASE_ISSIGNED(ComplexFloat);
+      CASE_ISSIGNED(ComplexDouble);
+      CASE_ISSIGNED(Bool);
+    case ScalarType::UInt1:
+    case ScalarType::UInt2:
+    case ScalarType::UInt3:
+    case ScalarType::UInt4:
+    case ScalarType::UInt5:
+    case ScalarType::UInt6:
+    case ScalarType::UInt7:
+      return true;
+    case ScalarType::Undefined:
+    case ScalarType::NumOptions:
+      break;
+      // Do not add default here, but rather define behavior of every new entry
+      // here.  `-Wswitch-enum` would raise a warning in those cases.
+  }
+  TORCH_CHECK(false, "Unknown ScalarType ", t);
+#undef CASE_ISSIGNED
+}
+
+inline bool isUnderlying(ScalarType type, ScalarType qtype) {
+  return type == toUnderlying(qtype);
+}
+
+inline ScalarType toRealValueType(ScalarType t) {
+  switch (t) {
+    case ScalarType::ComplexHalf:
+      return ScalarType::Half;
+    case ScalarType::ComplexFloat:
+      return ScalarType::Float;
+    case ScalarType::ComplexDouble:
+      return ScalarType::Double;
+    default:
+      return t;
+  }
+}
+
+inline ScalarType toComplexType(ScalarType t) {
+  switch (t) {
+    case ScalarType::BFloat16:
+      // BFloat16 has range equivalent to Float,
+      // so we map it to ComplexFloat.
+      return ScalarType::ComplexFloat;
+    case ScalarType::Half:
+      return ScalarType::ComplexHalf;
+    case ScalarType::Float:
+      return ScalarType::ComplexFloat;
+    case ScalarType::Double:
+      return ScalarType::ComplexDouble;
+    case ScalarType::ComplexHalf:
+      return ScalarType::ComplexHalf;
+    case ScalarType::ComplexFloat:
+      return ScalarType::ComplexFloat;
+    case ScalarType::ComplexDouble:
+      return ScalarType::ComplexDouble;
+    default:
+      TORCH_CHECK(false, "Unknown Complex ScalarType for ", t);
+  }
+}
+
+// see tensor_attributes.rst for detailed explanation and examples
+// of casting rules.
+inline bool canCast(const ScalarType from, const ScalarType to) {
+  // We disallow complex -> non complex, e.g., float_tensor *= complex is
+  // disallowed.
+  if (isComplexType(from) && !isComplexType(to)) {
+    return false;
+  }
+  // We disallow float -> integral, e.g., int_tensor *= float is disallowed.
+  if (isFloatingType(from) && isIntegralType(to, false)) {
+    return false;
+  }
+
+  // Treat bool as a distinct "category," to be consistent with type promotion
+  // rules (e.g. `bool_tensor + 5 -> int64_tensor`). If `5` was in the same
+  // category as `bool_tensor`, we would not promote. Differing categories
+  // implies `bool_tensor += 5` is disallowed.
+  //
+  // NB: numpy distinguishes "unsigned" as a category to get the desired
+  // `bool_tensor + 5 -> int64_tensor` behavior. We don't, because:
+  // * We don't want the performance hit of checking the runtime sign of
+  // Scalars.
+  // * `uint8_tensor + 5 -> int64_tensor` would be undesirable.
+  if (from != ScalarType::Bool && to == ScalarType::Bool) {
+    return false;
+  }
+  return true;
+}
+
+C10_API ScalarType promoteTypes(ScalarType a, ScalarType b);
+
+inline std::ostream& operator<<(
+    std::ostream& stream,
+    at::ScalarType scalar_type) {
+  return stream << toString(scalar_type);
+}
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/ScalarTypeToTypeMeta.h

@@ -0,0 +1,57 @@
+#pragma once
+
+#include <c10/core/ScalarType.h>
+#include <c10/util/Optional.h>
+#include <c10/util/typeid.h>
+
+// these just expose TypeMeta/ScalarType bridge functions in c10
+// TODO move to typeid.h (or codemod away) when TypeMeta et al
+// are moved from caffe2 to c10 (see note at top of typeid.h)
+
+namespace c10 {
+
+/**
+ * convert ScalarType enum values to TypeMeta handles
+ */
+inline caffe2::TypeMeta scalarTypeToTypeMeta(ScalarType scalar_type) {
+  return caffe2::TypeMeta::fromScalarType(scalar_type);
+}
+
+/**
+ * convert TypeMeta handles to ScalarType enum values
+ */
+inline ScalarType typeMetaToScalarType(caffe2::TypeMeta dtype) {
+  return dtype.toScalarType();
+}
+
+/**
+ * typeMetaToScalarType(), lifted to optional
+ */
+inline optional<at::ScalarType> optTypeMetaToScalarType(
+    optional<caffe2::TypeMeta> type_meta) {
+  if (!type_meta.has_value()) {
+    return c10::nullopt;
+  }
+  return type_meta->toScalarType();
+}
+
+/**
+ * convenience: equality across TypeMeta/ScalarType conversion
+ */
+inline bool operator==(ScalarType t, caffe2::TypeMeta m) {
+  return m.isScalarType(t);
+}
+
+inline bool operator==(caffe2::TypeMeta m, ScalarType t) {
+  return t == m;
+}
+
+inline bool operator!=(ScalarType t, caffe2::TypeMeta m) {
+  return !(t == m);
+}
+
+inline bool operator!=(caffe2::TypeMeta m, ScalarType t) {
+  return !(t == m);
+}
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/Storage.h

@@ -0,0 +1,272 @@
+#pragma once
+
+#include <c10/core/Allocator.h>
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/core/StorageImpl.h>
+#include <c10/core/SymInt.h>
+#include <c10/macros/Export.h>
+#include <c10/util/Exception.h>
+#include <c10/util/ExclusivelyOwned.h>
+#include <c10/util/MaybeOwned.h>
+#include <c10/util/UniqueVoidPtr.h>
+#include <c10/util/intrusive_ptr.h>
+#include <cstddef>
+#include <utility>
+
+namespace c10 {
+
+struct Storage;
+
+C10_API bool isSharedStorageAlias(
+    const Storage& storage0,
+    const Storage& storage1);
+
+struct C10_API Storage {
+ public:
+  struct use_byte_size_t {};
+  struct unsafe_borrow_t {
+    explicit unsafe_borrow_t() = default;
+  };
+
+  Storage() = default;
+  Storage(c10::intrusive_ptr<StorageImpl> ptr)
+      : storage_impl_(std::move(ptr)) {}
+
+  // Allocates memory buffer using given allocator and creates a storage with it
+  Storage(
+      use_byte_size_t /*use_byte_size*/,
+      const SymInt& size_bytes,
+      Allocator* allocator = nullptr,
+      bool resizable = false)
+      : storage_impl_(c10::make_intrusive<StorageImpl>(
+            StorageImpl::use_byte_size_t(),
+            size_bytes,
+            allocator,
+            resizable)) {}
+
+  // Creates storage with pre-allocated memory buffer. Allocator is given for
+  // potential future reallocations, however it can be nullptr if the storage
+  // is non-resizable
+  Storage(
+      use_byte_size_t /*use_byte_size*/,
+      size_t size_bytes,
+      at::DataPtr data_ptr,
+      at::Allocator* allocator = nullptr,
+      bool resizable = false)
+      : storage_impl_(c10::make_intrusive<StorageImpl>(
+            StorageImpl::use_byte_size_t(),
+            size_bytes,
+            std::move(data_ptr),
+            allocator,
+            resizable)) {}
+
+ protected:
+  explicit Storage(unsafe_borrow_t, const Storage& rhs)
+      : storage_impl_(c10::intrusive_ptr<c10::StorageImpl>::reclaim(
+            rhs.storage_impl_.get())) {}
+
+  friend MaybeOwnedTraits<Storage>;
+
+ public:
+  // Legacy constructor for partially initialized (dtype or memory) storages
+  // that can be temporarily created with Caffe2 APIs. See the note on top of
+  // TensorImpl.h for details.
+  static Storage create_legacy(at::Device device) {
+    auto allocator = GetAllocator(device.type());
+    return Storage(c10::make_intrusive<StorageImpl>(
+        StorageImpl::use_byte_size_t(),
+        0,
+        allocator->allocate(0), // materialize a non-default Device.
+        allocator,
+        true));
+  }
+
+  // Mimic create_legacy, but without requiring a newly-created StorageImpl.
+  void reset_legacy() {
+    TORCH_CHECK(resizable() && allocator());
+    set_nbytes(0);
+    set_data_ptr_noswap(allocator()->allocate(0));
+  }
+
+  // TODO: remove later
+  void set_nbytes(size_t size_bytes) const {
+    storage_impl_->set_nbytes(size_bytes);
+  }
+
+  void set_nbytes(c10::SymInt size_bytes) const {
+    storage_impl_->set_nbytes(std::move(size_bytes));
+  }
+
+  bool resizable() const {
+    return storage_impl_->resizable();
+  }
+
+  size_t nbytes() const {
+    return storage_impl_->nbytes();
+  }
+
+  SymInt sym_nbytes() const {
+    return storage_impl_->sym_nbytes();
+  }
+  // get() use here is to get const-correctness
+
+  const void* data() const {
+    return storage_impl_->data();
+  }
+
+  void* mutable_data() const {
+    return storage_impl_->mutable_data();
+  }
+
+  at::DataPtr& mutable_data_ptr() const {
+    return storage_impl_->mutable_data_ptr();
+  }
+
+  const at::DataPtr& data_ptr() const {
+    return storage_impl_->data_ptr();
+  }
+
+  // Returns the previous data_ptr
+  at::DataPtr set_data_ptr(at::DataPtr&& data_ptr) const {
+    return storage_impl_->set_data_ptr(std::move(data_ptr));
+  }
+
+  void set_data_ptr_noswap(at::DataPtr&& data_ptr) const {
+    return storage_impl_->set_data_ptr_noswap(std::move(data_ptr));
+  }
+
+  DeviceType device_type() const {
+    return storage_impl_->device_type();
+  }
+
+  at::Allocator* allocator() const {
+    return storage_impl_->allocator();
+  }
+
+  at::Device device() const {
+    return storage_impl_->device();
+  }
+
+  StorageImpl* unsafeReleaseStorageImpl() {
+    return storage_impl_.release();
+  }
+
+  StorageImpl* unsafeGetStorageImpl() const noexcept {
+    return storage_impl_.get();
+  }
+
+  c10::weak_intrusive_ptr<StorageImpl> getWeakStorageImpl() const {
+    return c10::weak_intrusive_ptr<StorageImpl>(storage_impl_);
+  }
+
+  operator bool() const {
+    return storage_impl_;
+  }
+
+  size_t use_count() const {
+    return storage_impl_.use_count();
+  }
+
+  inline bool unique() const {
+    return storage_impl_.unique();
+  }
+
+  bool is_alias_of(const Storage& other) const {
+    return (
+        storage_impl_ == other.storage_impl_ ||
+        isSharedStorageAlias(*this, other));
+  }
+
+  void UniqueStorageShareExternalPointer(
+      void* src,
+      size_t capacity,
+      DeleterFnPtr d = nullptr) {
+    if (!storage_impl_.unique()) {
+      TORCH_CHECK(
+          false,
+          "UniqueStorageShareExternalPointer can only be called when use_count == 1");
+    }
+    storage_impl_->UniqueStorageShareExternalPointer(src, capacity, d);
+  }
+
+  void UniqueStorageShareExternalPointer(
+      at::DataPtr&& data_ptr,
+      size_t capacity) {
+    if (!storage_impl_.unique()) {
+      TORCH_CHECK(
+          false,
+          "UniqueStorageShareExternalPointer can only be called when use_count == 1");
+    }
+    storage_impl_->UniqueStorageShareExternalPointer(
+        std::move(data_ptr), capacity);
+  }
+
+ protected:
+  c10::intrusive_ptr<StorageImpl> storage_impl_;
+};
+
+template <>
+struct MaybeOwnedTraits<c10::Storage> {
+  using owned_type = c10::Storage;
+  using borrow_type = c10::Storage;
+
+  static borrow_type createBorrow(const owned_type& from) {
+    return borrow_type(borrow_type::unsafe_borrow_t{}, from);
+  }
+
+  static void assignBorrow(borrow_type& lhs, const borrow_type& rhs) {
+    lhs.unsafeReleaseStorageImpl();
+    lhs = borrow_type(borrow_type::unsafe_borrow_t{}, rhs);
+  }
+
+  static void destroyBorrow(borrow_type& toDestroy) {
+    toDestroy.unsafeReleaseStorageImpl(); // "leak" it, but it was already +0.
+  }
+
+  static const owned_type& referenceFromBorrow(const borrow_type& borrow) {
+    return borrow;
+  }
+
+  static const owned_type* pointerFromBorrow(const borrow_type& borrow) {
+    return &borrow;
+  }
+
+  static bool debugBorrowIsValid(const borrow_type& /*borrow*/) {
+    return true;
+  }
+};
+
+template <>
+struct ExclusivelyOwnedTraits<c10::Storage> {
+  using repr_type = c10::Storage;
+  using pointer_type = c10::Storage*;
+  using const_pointer_type = const c10::Storage*;
+
+  static repr_type nullRepr() {
+    return c10::Storage();
+  }
+
+  template <class... Args>
+  static repr_type createInPlace(Args&&... args) {
+    return c10::Storage(std::forward<Args>(args)...);
+  }
+
+  static repr_type moveToRepr(c10::Storage&& x) {
+    return std::move(x);
+  }
+
+  static c10::Storage take(c10::Storage& x) {
+    return std::move(x);
+  }
+
+  static pointer_type getImpl(repr_type& x) {
+    return &x;
+  }
+
+  static const_pointer_type getImpl(const repr_type& x) {
+    return &x;
+  }
+};
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/StorageImpl.h

@@ -0,0 +1,330 @@
+#pragma once
+
+#include <c10/core/Allocator.h>
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/core/SymInt.h>
+#include <c10/core/impl/COW.h>
+#include <c10/core/impl/COWDeleter.h>
+#include <c10/core/impl/PyObjectSlot.h>
+#include <c10/macros/Export.h>
+#include <c10/util/Exception.h>
+#include <c10/util/UniqueVoidPtr.h>
+#include <c10/util/intrusive_ptr.h>
+#include <cstddef>
+#include <utility>
+
+namespace c10 {
+
+C10_API void throwNullDataPtrError();
+C10_API void warnDeprecatedDataPtr();
+
+// A storage represents the underlying backing data buffer for a
+// tensor.  This concept was inherited from the original Torch7
+// codebase; we'd kind of like to get rid of the concept
+// (see https://github.com/pytorch/pytorch/issues/14797) but
+// it's hard work and no one has gotten around to doing it.
+//
+// NB: storage is supposed to uniquely own a data pointer; e.g.,
+// two non-null data pointers alias if and only if they are from
+// the same storage.  Technically you can violate this invariant
+// (e.g., you can create a non-owning StorageImpl with at::from_blob)
+// but a lot of things won't work correctly, including:
+//
+// - An ordinary deleter on such a storage is wrong, because normal deleters
+//   assume unique ownership, but if you have two storages at the same data,
+//   that implies there is some sort of shared ownership. So your deleter would
+//   have to actually be internally doing some sort of refcount thing
+// - Deepcopy in Python side relies on storage equality and not data pointer
+//   equality; so if there are two separate storages pointing to the same data,
+//   the data will actually get duplicated in that case (one data ptr before,
+//   two data ptrs after)
+// - Version counts won't work correctly, because we do all VC tracking at the
+//   level of storages (unless you explicitly disconnect the VC with detach);
+//   mutation because data pointers are the same are totally untracked
+struct C10_API StorageImpl : public c10::intrusive_ptr_target {
+ public:
+  struct use_byte_size_t {};
+
+  StorageImpl(
+      use_byte_size_t /*use_byte_size*/,
+      SymInt size_bytes,
+      at::DataPtr data_ptr,
+      at::Allocator* allocator,
+      bool resizable)
+      : data_ptr_(std::move(data_ptr)),
+        size_bytes_(std::move(size_bytes)),
+        size_bytes_is_heap_allocated_(size_bytes_.is_heap_allocated()),
+        resizable_(resizable),
+        received_cuda_(false),
+        allocator_(allocator) {
+    if (resizable) {
+      TORCH_INTERNAL_ASSERT(
+          allocator_, "For resizable storage, allocator must be provided");
+    }
+    refresh_has_data_ptr_check();
+  }
+
+  StorageImpl(
+      use_byte_size_t /*use_byte_size*/,
+      const SymInt& size_bytes,
+      at::Allocator* allocator,
+      bool resizable)
+      : StorageImpl(
+            use_byte_size_t(),
+            size_bytes,
+            size_bytes.is_heap_allocated()
+                ? allocator->allocate(0)
+                : allocator->allocate(size_bytes.as_int_unchecked()),
+            allocator,
+            resizable) {}
+
+  StorageImpl& operator=(StorageImpl&& other) = delete;
+  StorageImpl& operator=(const StorageImpl&) = delete;
+  StorageImpl() = delete;
+  StorageImpl(StorageImpl&& other) = delete;
+  StorageImpl(const StorageImpl&) = delete;
+  ~StorageImpl() override = default;
+
+  void reset() {
+    data_ptr_.clear();
+    size_bytes_ = 0;
+    size_bytes_is_heap_allocated_ = false;
+  }
+
+  // Destructor doesn't call release_resources because it's
+  // unnecessary; don't forget to change that if needed!
+  void release_resources() override {
+    data_ptr_.clear();
+  }
+
+  size_t nbytes() const {
+    // OK to do this instead of maybe_as_int as nbytes is guaranteed positive
+    TORCH_CHECK(!size_bytes_is_heap_allocated_);
+    return size_bytes_.as_int_unchecked();
+  }
+
+  SymInt sym_nbytes() const {
+    return size_bytes_;
+  }
+
+  // TODO: remove later
+  void set_nbytes(size_t size_bytes) {
+    size_bytes_ = static_cast<int64_t>(size_bytes);
+    size_bytes_is_heap_allocated_ = false;
+  }
+
+  void set_nbytes(c10::SymInt size_bytes) {
+    size_bytes_ = std::move(size_bytes);
+  }
+
+  bool resizable() const {
+    return resizable_;
+  }
+
+  const at::DataPtr& data_ptr() const {
+    return data_ptr_;
+  }
+
+  at::DataPtr& mutable_data_ptr() {
+    if (C10_UNLIKELY(has_data_ptr_check_)) {
+      if (throw_on_mutable_data_ptr_) {
+        throwNullDataPtrError();
+      }
+      if (warn_deprecated_on_mutable_data_ptr_) {
+        warnDeprecatedDataPtr();
+      }
+      maybe_materialize_cow();
+    }
+    return data_ptr_;
+  }
+
+  // Returns the data_ptr. Bypasses all checks.
+  at::DataPtr& _mutable_data_ptr_no_checks() {
+    return data_ptr_;
+  }
+
+  // Returns the previous data_ptr
+  at::DataPtr set_data_ptr(at::DataPtr&& data_ptr) {
+    // We need to materialize the old COW DataPtr because it is
+    // being returned as mutable.
+    maybe_materialize_cow();
+    return set_data_ptr_no_materialize_cow(std::move(data_ptr));
+  }
+
+  void set_data_ptr_noswap(at::DataPtr&& data_ptr) {
+    data_ptr_ = std::move(data_ptr);
+    refresh_has_data_ptr_check();
+  }
+
+  const void* data() const {
+    return data_ptr_.get();
+  }
+
+  void* mutable_data() {
+    if (C10_UNLIKELY(has_data_ptr_check_)) {
+      if (throw_on_mutable_data_ptr_) {
+        throwNullDataPtrError();
+      }
+      if (warn_deprecated_on_mutable_data_ptr_) {
+        warnDeprecatedDataPtr();
+      }
+      maybe_materialize_cow();
+    }
+    return data_ptr_.mutable_get();
+  }
+
+  at::DeviceType device_type() const {
+    return data_ptr_.device().type();
+  }
+
+  at::Allocator* allocator() {
+    return allocator_;
+  }
+
+  const at::Allocator* allocator() const {
+    return allocator_;
+  }
+
+  // You generally shouldn't use this method, but it is occasionally
+  // useful if you want to override how a tensor will be reallocated,
+  // after it was already allocated (and its initial allocator was
+  // set)
+  void set_allocator(at::Allocator* allocator) {
+    allocator_ = allocator;
+  }
+
+  Device device() const {
+    return data_ptr_.device();
+  }
+
+  void set_resizable(bool resizable) {
+    if (resizable) {
+      // We need an allocator to be resizable
+      AT_ASSERT(allocator_);
+    }
+    resizable_ = resizable;
+  }
+
+  /**
+   * Can only be called when use_count is 1
+   */
+  void UniqueStorageShareExternalPointer(
+      void* src,
+      size_t size_bytes,
+      DeleterFnPtr d = nullptr) {
+    UniqueStorageShareExternalPointer(
+        at::DataPtr(src, src, d, data_ptr_.device()), size_bytes);
+  }
+
+  /**
+   * Can only be called when use_count is 1
+   */
+  void UniqueStorageShareExternalPointer(
+      at::DataPtr&& data_ptr,
+      size_t size_bytes) {
+    data_ptr_ = std::move(data_ptr);
+    size_bytes_ = static_cast<int64_t>(size_bytes);
+    size_bytes_is_heap_allocated_ = false;
+    allocator_ = nullptr;
+    resizable_ = false;
+  }
+
+  // This method can be used only after storage construction and cannot be used
+  // to modify storage status
+  void set_received_cuda(bool received_cuda) {
+    received_cuda_ = received_cuda;
+  }
+
+  bool received_cuda() {
+    return received_cuda_;
+  }
+
+  impl::PyObjectSlot* pyobj_slot() {
+    return &pyobj_slot_;
+  }
+
+  const impl::PyObjectSlot* pyobj_slot() const {
+    return &pyobj_slot_;
+  }
+
+  void set_throw_on_mutable_data_ptr() {
+    throw_on_mutable_data_ptr_ = true;
+    refresh_has_data_ptr_check();
+  }
+
+  void set_warn_deprecated_on_mutable_data_ptr() {
+    warn_deprecated_on_mutable_data_ptr_ = true;
+    refresh_has_data_ptr_check();
+  }
+
+ protected:
+  // materialize_cow_storage needs to call set_data_ptr_no_materlize_cow
+  friend void c10::impl::cow::materialize_cow_storage(StorageImpl& storage);
+
+  // Returns the previous data_ptr. If the old data_ptr was COW,
+  // this avoids materializing it
+  at::DataPtr set_data_ptr_no_materialize_cow(at::DataPtr&& data_ptr) {
+    at::DataPtr old_data_ptr(std::move(data_ptr_));
+    data_ptr_ = std::move(data_ptr);
+    refresh_has_data_ptr_check();
+    return old_data_ptr;
+  }
+
+ private:
+  void refresh_has_data_ptr_check() {
+    has_data_ptr_check_ = is_cow() || throw_on_mutable_data_ptr_ ||
+        warn_deprecated_on_mutable_data_ptr_;
+  }
+
+  inline bool is_cow() const {
+    return c10::impl::cow::is_cow_data_ptr(data_ptr_);
+  }
+
+  // Triggers a copy if this is a copy-on-write tensor.
+  void maybe_materialize_cow() {
+    if (is_cow()) {
+      impl::cow::materialize_cow_storage(*this);
+    }
+  }
+
+  DataPtr data_ptr_;
+  SymInt size_bytes_;
+  bool size_bytes_is_heap_allocated_;
+  bool resizable_;
+  // Identifies that Storage was received from another process and doesn't have
+  // local to process cuda memory allocation
+  bool received_cuda_;
+  // All special checks in data/data_ptr calls are guarded behind this single
+  // boolean. This is for performance: .data/.data_ptr calls are commonly in the
+  // hot-path.
+  bool has_data_ptr_check_ = false;
+  // If we should throw when mutable_data_ptr() or mutable_data() is called.
+  bool throw_on_mutable_data_ptr_ = false;
+  // If we warn when mutable_data_ptr() or mutable_data() is called.
+  bool warn_deprecated_on_mutable_data_ptr_ = false;
+  Allocator* allocator_;
+  impl::PyObjectSlot pyobj_slot_;
+};
+
+// Declare StorageImpl create function pointer types.
+using StorageImplCreateHelper = intrusive_ptr<StorageImpl> (*)(
+    StorageImpl::use_byte_size_t,
+    SymInt size_bytes,
+    DataPtr data_ptr,
+    Allocator* allocator,
+    bool resizable);
+
+C10_API void SetStorageImplCreate(DeviceType t, StorageImplCreateHelper fptr);
+
+C10_API StorageImplCreateHelper GetStorageImplCreate(DeviceType t);
+
+C10_API c10::intrusive_ptr<c10::StorageImpl> make_storage_impl(
+    c10::StorageImpl::use_byte_size_t use_byte_size,
+    c10::SymInt size_bytes,
+    c10::DataPtr data_ptr,
+    c10::Allocator* allocator,
+    bool resizable,
+    std::optional<at::Device> device_opt);
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/Stream.h

@@ -0,0 +1,176 @@
+#pragma once
+
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/macros/Export.h>
+#include <c10/util/Exception.h>
+#include <cstddef>
+#include <cstdint>
+#include <functional>
+#include <ostream>
+
+namespace c10 {
+
+/// An index representing a specific stream.  A StreamId is not independently
+/// meaningful without knowing the Device it is associated with; try to
+/// use Stream rather than StreamId directly.
+///
+/// StreamIds are opaque; they are assigned by some DeviceType-specific
+/// numbering system which is not visible to the user.  HOWEVER, we
+/// guarantee that StreamId 0 is always a valid stream, and corresponds
+/// to some sort of "default" stream.
+using StreamId = int64_t;
+
+struct C10_API StreamData3 {
+  StreamId stream_id;
+  DeviceIndex device_index;
+  DeviceType device_type;
+};
+
+// NB: I decided not to call the above StreamIndex to avoid confusion with
+// DeviceIndex.  This way, you access device index with index(), and stream id
+// with id()
+
+/**
+ * A stream is a software mechanism used to synchronize launched kernels
+ * without requiring explicit synchronizations between kernels.  The basic
+ * model is that every kernel launch is associated with a stream: every
+ * kernel on the same stream is implicitly synchronized so that if I launch
+ * kernels A and B on the same stream, A is guaranteed to finish before B
+ * launches.  If I want B to run concurrently with A, I must schedule
+ * it on a different stream.
+ *
+ * The Stream class is a backend agnostic value class representing a stream
+ * which I may schedule a kernel on.  Every stream is associated with a device,
+ * which is recorded in stream, which is used to avoid confusion about which
+ * device a stream refers to.
+ *
+ * Streams are explicitly thread-safe, in the sense that it is OK to pass
+ * a Stream from one thread to another, and kernels queued from two different
+ * threads will still get serialized appropriately.  (Of course, the
+ * time when the kernels get queued is undetermined unless you synchronize
+ * host side ;)
+ *
+ * Stream does NOT have a default constructor.  Streams are for expert
+ * users; if you want to use Streams, we're going to assume you know
+ * how to deal with C++ template error messages if you try to
+ * resize() a vector of Streams.
+ *
+ * Known instances of streams in backends:
+ *
+ *  - cudaStream_t (CUDA)
+ *  - hipStream_t (HIP)
+ *  - cl_command_queue (OpenCL)  (NB: Caffe2's existing OpenCL integration
+ *    does NOT support command queues.)
+ *
+ * Because this class is device agnostic, it cannot provide backend-specific
+ * functionality (e.g., get the cudaStream_t of a CUDA stream.)  There are
+ * wrapper classes which provide this functionality, e.g., CUDAStream.
+ */
+class C10_API Stream final {
+ private:
+  Device device_;
+  StreamId id_;
+
+ public:
+  enum Unsafe { UNSAFE };
+  enum Default { DEFAULT };
+
+  /// Unsafely construct a stream from a Device and a StreamId.  In
+  /// general, only specific implementations of streams for a
+  /// backend should manufacture Stream directly in this way; other users
+  /// should use the provided APIs to get a stream.  In particular,
+  /// we don't require backends to give any guarantees about non-zero
+  /// StreamIds; they are welcome to allocate in whatever way they like.
+  explicit Stream(Unsafe, Device device, StreamId id)
+      : device_(device), id_(id) {}
+
+  /// Construct the default stream of a Device.  The default stream is
+  /// NOT the same as the current stream; default stream is a fixed stream
+  /// that never changes, whereas the current stream may be changed by
+  /// StreamGuard.
+  explicit Stream(Default, Device device) : device_(device), id_(0) {}
+
+  bool operator==(const Stream& other) const noexcept {
+    return this->device_ == other.device_ && this->id_ == other.id_;
+  }
+  bool operator!=(const Stream& other) const noexcept {
+    return !(*this == other);
+  }
+
+  Device device() const noexcept {
+    return device_;
+  }
+  DeviceType device_type() const noexcept {
+    return device_.type();
+  }
+  DeviceIndex device_index() const noexcept {
+    return device_.index();
+  }
+  StreamId id() const noexcept {
+    return id_;
+  }
+
+  // Enqueues a wait instruction in the stream's work queue.
+  // This instruction is a no-op unless the event is marked
+  // for recording. In that case the stream stops processing
+  // until the event is recorded.
+  template <typename T>
+  void wait(const T& event) const {
+    event.block(*this);
+  }
+
+  // Return whether all asynchronous work previously enqueued on this stream
+  // has completed running on the device.
+  bool query() const;
+
+  // Wait (by blocking the calling thread) until all asynchronous work enqueued
+  // on this stream has completed running on the device.
+  void synchronize() const;
+
+  // The purpose of this function is to more conveniently permit binding
+  // of Stream to and from Python.  Without packing, I have to setup a whole
+  // class with two fields (device and stream id); with packing I can just
+  // store a single uint64_t.
+  //
+  // The particular way we pack streams into a uint64_t is considered an
+  // implementation detail and should not be relied upon.
+  uint64_t hash() const noexcept {
+    // Concat these together into a 64-bit integer
+    uint64_t bits = static_cast<uint64_t>(device_type()) << 56 |
+        static_cast<uint64_t>(device_index()) << 48 |
+        // Remove the sign extension part of the 64-bit address because
+        // the id might be used to hold a pointer.
+        (static_cast<uint64_t>(id()) & ((1ull << 48) - 1));
+    return bits;
+  }
+
+  struct StreamData3 pack3() const {
+    return {id(), device_index(), device_type()};
+  }
+
+  static Stream unpack3(
+      StreamId stream_id,
+      DeviceIndex device_index,
+      DeviceType device_type) {
+    TORCH_CHECK(isValidDeviceType(device_type));
+    return Stream(UNSAFE, Device(device_type, device_index), stream_id);
+  }
+
+  // I decided NOT to provide setters on this class, because really,
+  // why would you change the device of a stream?  Just construct
+  // it correctly from the beginning dude.
+};
+
+C10_API std::ostream& operator<<(std::ostream& stream, const Stream& s);
+
+} // namespace c10
+
+namespace std {
+template <>
+struct hash<c10::Stream> {
+  size_t operator()(c10::Stream s) const noexcept {
+    return std::hash<uint64_t>{}(s.hash());
+  }
+};
+} // namespace std

parrot/lib/python3.10/site-packages/torch/include/c10/core/StreamGuard.h

@@ -0,0 +1,170 @@
+#pragma once
+
+#include <c10/core/Device.h>
+#include <c10/core/Stream.h>
+#include <c10/core/impl/InlineStreamGuard.h>
+#include <c10/core/impl/VirtualGuardImpl.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/Optional.h>
+
+namespace c10 {
+
+/**
+ * A StreamGuard is an RAII class that changes the current device
+ * to the device corresponding to some stream, and changes the
+ * default stream on that device to be this stream.
+ *
+ * Use of StreamGuard is HIGHLY discouraged in operator definitions.  In
+ * a single operator, you probably don't know enough about the global
+ * state of the world to profitably decide how to set streams.  Let
+ * the caller handle this appropriately, and just use the current stream
+ * in your operator code.
+ *
+ * This StreamGuard does NOT have an uninitialized state; it is guaranteed
+ * to reset the stream and device on exit.  If you are in a situation
+ * where you *might* want to setup a stream guard, see OptionalStreamGuard.
+ */
+struct StreamGuard {
+  /// No default constructor, see Note [Omitted default constructor from RAII]
+  explicit StreamGuard() = delete;
+
+  /// Set the current device to the device associated with the passed stream,
+  /// and set the current  stream on that device to the passed stream.
+  explicit StreamGuard(Stream stream) : guard_(stream) {}
+
+  /// Copy is disallowed
+  StreamGuard(const StreamGuard&) = delete;
+  StreamGuard& operator=(const StreamGuard&) = delete;
+
+  /// Move is disallowed, as StreamGuard does not have an uninitialized state,
+  /// which is required for moves on types with nontrivial destructors.
+  StreamGuard(StreamGuard&& other) = delete;
+  StreamGuard& operator=(StreamGuard&& other) = delete;
+
+  /// Resets the currently set stream to the original stream and
+  /// the currently set device to the original device.  Then,
+  /// set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream.
+  ///
+  /// NOTE: this implementation may skip some stream/device setting if
+  /// it can prove that it is unnecessary.
+  ///
+  /// WARNING: reset_stream does NOT preserve previously set streams on
+  /// different devices.  If you need to set streams on multiple devices
+  /// on , use MultiStreamGuard instead.
+  void reset_stream(Stream stream) {
+    guard_.reset_stream(stream);
+  }
+
+  /// Returns the stream that was set at the time the guard was constructed.
+  Stream original_stream() const {
+    return guard_.original_stream();
+  }
+
+  /// Returns the most recent stream that was set using this device guard,
+  /// either from construction, or via set_stream.
+  Stream current_stream() const {
+    return guard_.current_stream();
+  }
+
+  /// Returns the most recent device that was set using this device guard,
+  /// either from construction, or via set_device/reset_device/set_index.
+  Device current_device() const {
+    return guard_.current_device();
+  }
+
+  /// Returns the device that was set at the most recent reset_stream(),
+  /// or otherwise the device at construction time.
+  Device original_device() const {
+    return guard_.original_device();
+  }
+
+ private:
+  c10::impl::InlineStreamGuard<impl::VirtualGuardImpl> guard_;
+};
+
+/**
+ * An OptionalStreamGuard is an RAII class that sets a device to some value on
+ * initialization, and resets the device to its original value on destruction.
+ * See OptionalDeviceGuard for more guidance on how to use this class.
+ */
+struct OptionalStreamGuard {
+  /// Create an uninitialized guard.
+  explicit OptionalStreamGuard() = default;
+
+  /// Set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream.
+  explicit OptionalStreamGuard(Stream stream) : guard_(stream) {}
+
+  /// Set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream,
+  /// if the passed stream is not nullopt.
+  explicit OptionalStreamGuard(optional<Stream> stream_opt)
+      : guard_(stream_opt) {}
+
+  /// Copy is disallowed
+  OptionalStreamGuard(const OptionalStreamGuard&) = delete;
+  OptionalStreamGuard& operator=(const OptionalStreamGuard&) = delete;
+
+  // See Note [Move construction for RAII guards is tricky]
+  OptionalStreamGuard(OptionalStreamGuard&& other) = delete;
+
+  // See Note [Move assignment for RAII guards is tricky]
+  OptionalStreamGuard& operator=(OptionalStreamGuard&& other) = delete;
+
+  /// Resets the currently set stream to the original stream and
+  /// the currently set device to the original device.  Then,
+  /// set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream.
+  /// Initializes the guard if it was not previously initialized.
+  void reset_stream(Stream stream) {
+    guard_.reset_stream(stream);
+  }
+
+  /// Returns the stream that was set at the time the guard was most recently
+  /// initialized, or nullopt if the guard is uninitialized.
+  optional<Stream> original_stream() const {
+    return guard_.original_stream();
+  }
+
+  /// Returns the most recent  stream that was set using this stream guard,
+  /// either from construction, or via reset_stream, if the guard is
+  /// initialized, or nullopt if the guard is uninitialized.
+  optional<Stream> current_stream() const {
+    return guard_.current_stream();
+  }
+
+  /// Restore the original  device and stream, resetting this guard to
+  /// uninitialized state.
+  void reset() {
+    guard_.reset();
+  }
+
+ private:
+  c10::impl::InlineOptionalStreamGuard<impl::VirtualGuardImpl> guard_{};
+};
+
+/**
+ * A MultiStreamGuard is an RAII class that sets the current streams of a set of
+ * devices all at once, and resets them to their original values on destruction.
+ */
+struct MultiStreamGuard {
+  /// Set the current streams to the passed streams on each of their respective
+  /// devices.
+  explicit MultiStreamGuard(ArrayRef<Stream> streams) : guard_(streams) {}
+
+  /// Copy is disallowed
+  MultiStreamGuard(const MultiStreamGuard&) = delete;
+  MultiStreamGuard& operator=(const MultiStreamGuard&) = delete;
+
+  // See Note [Move construction for RAII guards is tricky]
+  MultiStreamGuard(MultiStreamGuard&& other) = delete;
+
+  // See Note [Move assignment for RAII guards is tricky]
+  MultiStreamGuard& operator=(MultiStreamGuard&& other) = delete;
+
+ private:
+  c10::impl::InlineMultiStreamGuard<impl::VirtualGuardImpl> guard_;
+};
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/SymFloat.h

@@ -0,0 +1,113 @@
+#pragma once
+
+#include <c10/core/SymBool.h>
+#include <c10/core/SymNodeImpl.h>
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/intrusive_ptr.h>
+
+#include <cstdint>
+#include <limits>
+#include <ostream>
+#include <utility>
+
+namespace c10 {
+
+// NB: this is actually double precision; we're using the Python naming here
+class C10_API SymFloat {
+ public:
+  /*implicit*/ SymFloat(double d) : data_(d){};
+  SymFloat(SymNode ptr)
+      : data_(std::numeric_limits<double>::quiet_NaN()), ptr_(std::move(ptr)) {
+    TORCH_CHECK(ptr_->is_float());
+  };
+  SymFloat() : data_(0.0) {}
+
+  SymNodeImpl* toSymNodeImplUnowned() const {
+    return ptr_.get();
+  }
+
+  SymNodeImpl* release() && {
+    return std::move(ptr_).release();
+  }
+
+  // Only valid if is_symbolic()
+  SymNode toSymNodeImpl() const;
+
+  // Guaranteed to return a SymNode, wrapping using base if necessary
+  SymNode wrap_node(const SymNode& base) const;
+
+  double expect_float() const {
+    TORCH_CHECK(!is_symbolic());
+    return data_;
+  }
+
+  SymFloat operator+(const SymFloat&) const;
+  SymFloat operator-(const SymFloat&) const;
+  SymFloat operator*(const SymFloat&) const;
+  SymFloat operator/(const SymFloat&) const;
+
+  SymBool sym_eq(const SymFloat&) const;
+  SymBool sym_ne(const SymFloat&) const;
+  SymBool sym_lt(const SymFloat&) const;
+  SymBool sym_le(const SymFloat&) const;
+  SymBool sym_gt(const SymFloat&) const;
+  SymBool sym_ge(const SymFloat&) const;
+
+  bool operator==(const SymFloat& o) const {
+    return sym_eq(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator!=(const SymFloat& o) const {
+    return sym_ne(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator<(const SymFloat& o) const {
+    return sym_lt(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator<=(const SymFloat& o) const {
+    return sym_le(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator>(const SymFloat& o) const {
+    return sym_gt(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator>=(const SymFloat& o) const {
+    return sym_ge(o).guard_bool(__FILE__, __LINE__);
+  }
+
+  SymFloat min(const SymFloat& sci) const;
+  SymFloat max(const SymFloat& sci) const;
+
+  // Need guidance on where to put this code
+  SymFloat sqrt() const;
+
+  // Insert a guard for the float to be its concrete value, and then return
+  // that value.  This operation always works, even if the float is symbolic,
+  // so long as we know what the underlying value is. Don't blindly put this
+  // everywhere; you can cause overspecialization of PyTorch programs with
+  // this method.
+  //
+  // It should be called as guard_float(__FILE__, __LINE__).  The file and line
+  // number can be used to diagnose overspecialization.
+  double guard_float(const char* file, int64_t line) const;
+
+  bool has_hint() const;
+
+  // N.B. It's important to keep this definition in the header
+  // as we expect if checks to be folded for mobile builds
+  // where `is_symbolic` is always false
+  C10_ALWAYS_INLINE bool is_symbolic() const {
+    return ptr_;
+  }
+
+  double as_float_unchecked() const {
+    return data_;
+  }
+
+ private:
+  // TODO: optimize to union
+  double data_;
+  SymNode ptr_;
+};
+
+C10_API std::ostream& operator<<(std::ostream& os, const SymFloat& s);
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/SymInt.h

@@ -0,0 +1,423 @@
+#pragma once
+
+#include <c10/core/SymBool.h>
+#include <c10/core/SymNodeImpl.h>
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Optional.h>
+
+#include <cstdint>
+#include <iterator>
+#include <numeric>
+#include <ostream>
+#include <type_traits>
+
+namespace c10 {
+
+class SymFloat;
+
+// SymInt represents either a regular int64_t, or a symbolic integer
+// (represented in a type erased way as SymNode).  The intention is for SymInt
+// to represent symbolic sizes that arise when doing shape computation in
+// operator kernels. This allows for tracing through programs without baking in
+// concrete sizes into kernel calls.
+//
+// SymInt has an API equivalent to int64_t.  In particular, it is a value type.
+// Internally, SymInt is represented in a clever packed way, so that it only
+// occupies one word of space; but morally, it is a union between an int64_t
+// and an intrusive pointer to SymNodeImpl.
+//
+// Invariant: the referenced SymNodeImpl is guaranteed to be a SymNode where
+// is_int() returns true
+
+class C10_API SymInt {
+ public:
+  enum Unchecked {
+    UNCHECKED,
+  };
+
+  /*implicit*/ SymInt(int64_t d) : data_(d) {
+    if (is_heap_allocated()) {
+      // Large negative number, heap allocate it
+      promote_to_negative();
+    }
+  };
+  SymInt() : data_(0) {}
+  SymInt(SymNode n);
+
+  // unchecked c-tor accepting raw `data_`
+  // One appropriate use for this is when you are constructing a symint
+  // in a situation where you know it is non-negative (or, if it is negative,
+  // the negative value is -1; i.e., not user controlled)
+  SymInt(Unchecked, int64_t d) : data_(d) {}
+
+  // TODO: these implementations are not optimal because they allocate a
+  // temporary and then use the move constructor/assignment
+  SymInt(const SymInt& s) : data_(0) {
+    if (s.is_heap_allocated()) {
+      *this = SymInt(s.toSymNode());
+    } else {
+      data_ = s.data_;
+    }
+  }
+  SymInt(SymInt&& s) noexcept : data_(s.data_) {
+    s.data_ = 0;
+  }
+
+  SymInt& operator=(const SymInt& s) {
+    if (this != &s) {
+      if (s.is_heap_allocated()) {
+        *this = SymInt(s.toSymNode());
+      } else {
+        data_ = s.data_;
+      }
+    }
+    return *this;
+  }
+  SymInt& operator=(SymInt&& s) noexcept {
+    if (this != &s) {
+      release_(); // release the current SymNode if any
+      data_ = s.data_;
+      if (s.is_heap_allocated())
+        s.data_ = 0;
+    };
+    return *this;
+  }
+
+  SymNodeImpl* toSymNodeImplUnowned() const {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(is_heap_allocated());
+    uint64_t unextended_bits = static_cast<uint64_t>(data_) & ~MASK;
+    uint64_t sign_bit_mask = 1ULL << (62 - 1);
+    // https://stackoverflow.com/questions/42534749/signed-extension-from-24-bit-to-32-bit-in-c
+    uint64_t extended_bits = (unextended_bits ^ sign_bit_mask) - sign_bit_mask;
+    return static_cast<SymNodeImpl*>(
+        // NOLINTNEXTLINE(performance-no-int-to-ptr)
+        reinterpret_cast<void*>(static_cast<uintptr_t>(extended_bits)));
+  }
+
+  void release_() {
+    if (is_heap_allocated()) {
+      SymNode::reclaim(toSymNodeImplUnowned()); // steal
+    }
+  }
+
+  SymNodeImpl* release() && {
+#ifndef C10_MOBILE
+    TORCH_INTERNAL_ASSERT(is_heap_allocated());
+    auto* r = toSymNodeImplUnowned();
+    data_ = 0; // transfer ownership
+    return r;
+#else
+    TORCH_INTERNAL_ASSERT(false);
+#endif
+  }
+
+  // Only valid if is_heap_allocated()
+  SymNode toSymNode() const;
+
+  // Guaranteed to return a SymNode, wrapping using base if necessary
+  SymNode wrap_node(const SymNode& base) const;
+
+  ~SymInt() {
+    release_();
+  }
+
+  // Require the int to be non-symbolic, and if it is symbolic raise an
+  // error.  This is safe to use for C++ code that doesn't work for symbolic
+  // shapes, and you don't have time to fix it immediately, as if we
+  // try to trigger the path in C++ you'll appropriately get an error
+  int64_t expect_int() const {
+    if (auto r = maybe_as_int()) {
+      return *r;
+    }
+    TORCH_CHECK_ALWAYS_SHOW_CPP_STACKTRACE(
+        false, "when unpacking SymInt, expected int but got ", *this);
+  }
+
+  // Test if we have a hint for this int (e.g., guard_int would work).
+  // Most of the time this is true; it is only false when you have
+  // an unbacked SymInt.
+  bool has_hint() const;
+
+  // Insert a guard for the int to be its concrete value, and then return
+  // that value.  This operation always works, even if the int is symbolic,
+  // so long as we know what the underlying value is (e.g., this won't work
+  // if you call it on the size of nonzero output).  Don't blindly put this
+  // everywhere; you can cause overspecialization of PyTorch programs with
+  // this method.
+  //
+  // It should be called as guard_int(__FILE__, __LINE__).  The file and line
+  // number can be used to diagnose overspecialization.
+  int64_t guard_int(const char* file, int64_t line) const;
+
+  // Insert a guard that this SymInt must be size-like, returning true if
+  // the integer actually is >= 0.  Unlike manually performing a >= 0 test,
+  // if the SymInt in question is an unbacked SymInt (or, potentially in the
+  // future, if it contains unbacked SymInts), we will also treat the
+  // unbacked SymInt as statically testing >= 2 (which will prevent us from
+  // choking on, e.g., contiguity checks.)
+  bool expect_size(const char* file, int64_t line) const;
+
+  // Distinguish actual symbolic values from constants stored on the heap
+  bool is_symbolic() const {
+    return is_heap_allocated() &&
+        !toSymNodeImplUnowned()->constant_int().has_value();
+  }
+
+  // N.B. It's important to keep this definition in the header
+  // as we expect if checks to be folded for mobile builds
+  // where `is_heap_allocated` is always false and optimize dead code paths
+  C10_ALWAYS_INLINE bool is_heap_allocated() const {
+#ifdef C10_MOBILE
+    return false;
+#else
+    return !check_range(data_);
+#endif
+  }
+
+  SymInt operator+(const SymInt& sci) const;
+  SymInt operator-(const SymInt& sci) const;
+  SymInt operator*(const SymInt& sci) const;
+  SymInt operator/(const SymInt& sci) const;
+  SymInt operator%(const SymInt& sci) const;
+  void operator*=(const SymInt& sci);
+  void operator+=(const SymInt& sci);
+  void operator/=(const SymInt& sci);
+
+  SymInt clone() const;
+
+  SymBool sym_eq(const SymInt&) const;
+  SymBool sym_ne(const SymInt&) const;
+  SymBool sym_lt(const SymInt&) const;
+  SymBool sym_le(const SymInt&) const;
+  SymBool sym_gt(const SymInt&) const;
+  SymBool sym_ge(const SymInt&) const;
+
+  bool operator==(const SymInt& o) const {
+    return sym_eq(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator!=(const SymInt& o) const {
+    return sym_ne(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator<(const SymInt& o) const {
+    return sym_lt(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator<=(const SymInt& o) const {
+    return sym_le(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator>(const SymInt& o) const {
+    return sym_gt(o).guard_bool(__FILE__, __LINE__);
+  }
+  bool operator>=(const SymInt& o) const {
+    return sym_ge(o).guard_bool(__FILE__, __LINE__);
+  }
+
+  SymInt min(const SymInt& sci) const;
+  SymInt max(const SymInt& sci) const;
+
+  // If both are symbolic, this checks if
+  // they share the same node.
+  // If both are not symbolic this just checks normal equality.
+  bool is_same(const SymInt& other) const;
+
+  operator SymFloat() const;
+
+  // Don't use this.  Prefer maybe_as_int instead
+  int64_t as_int_unchecked() const {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(!is_heap_allocated());
+    return data_;
+  }
+
+  std::optional<int64_t> maybe_as_int() const {
+    if (!is_heap_allocated()) {
+      return c10::make_optional(data_);
+    }
+    auto* node = toSymNodeImplUnowned();
+    if (auto c = node->constant_int()) {
+      return c;
+    }
+    return node->maybe_as_int();
+  }
+
+  // Return whether the integer is directly coercible to a SymInt
+  // without requiring heap allocation.  You don't need to use this
+  // to check if you can pass an integer to SymInt; this is guaranteed
+  // to work (it just might heap allocate!)
+  static bool check_range(int64_t i) {
+    return i > MAX_UNREPRESENTABLE_INT;
+  }
+
+  // Return the min representable integer as a SymInt without
+  // heap allocation.  For quantities that count bytes (or larger),
+  // this is still much larger than you need, so you may consider
+  // using this as a more efficient version of MIN_INT
+  static constexpr int64_t min_representable_int() {
+    return MAX_UNREPRESENTABLE_INT + 1;
+  }
+
+ private:
+  void promote_to_negative();
+
+  // Constraints on the internal representation:
+  //
+  // - Should represent positive and small negative ints
+  // - No conversion necessary for operations on ints
+  // - Must represent valid 64-bit pointers
+  // - Is symbolic test should be FAST (two arithmetic instructions is too
+  // much).
+  //   This code being a hotpath is based on Strobelight profiles of
+  //   is_heap_allocated().  FB only: https://fburl.com/strobelight/5l50ncxd
+  //   (you will need to change the time window).
+  //
+  // So, the scheme is to reserve large negative numbers (assuming
+  // two's complement):
+  //
+  // - 0b0.... means we are a positive int
+  // - 0b11... means we are a small negative int
+  // - 0b10... means we are are a pointer. This means that
+  //           [-2^63, -2^62-1] are not representable as ints.
+  //           We don't actually need all of this space as on x86_64
+  //           as the top 16bits aren't used for anything
+  static constexpr uint64_t MASK = 1ULL << 63 | 1ULL << 62 | 1ULL << 61;
+  static constexpr uint64_t IS_SYM = 1ULL << 63 | 1ULL << 61;
+  // We must manually translate the bit pattern test into a greater
+  // than test because compiler doesn't figure it out:
+  // https://godbolt.org/z/356aferaW
+  static constexpr int64_t MAX_UNREPRESENTABLE_INT =
+      -1LL & static_cast<int64_t>(~(1ULL << 62));
+  int64_t data_;
+};
+
+/// Sum of a list of SymInt; accumulates into the c10::SymInt expression
+template <
+    typename C,
+    typename std::enable_if_t<
+        std::is_same_v<typename C::value_type, c10::SymInt>,
+        int> = 0>
+inline c10::SymInt multiply_integers(const C& container) {
+  return std::accumulate(
+      container.begin(),
+      container.end(),
+      c10::SymInt(1),
+      [](const c10::SymInt& a, const c10::SymInt& b) { return a * b; });
+}
+
+template <
+    typename Iter,
+    typename = std::enable_if_t<std::is_same_v<
+        typename std::iterator_traits<Iter>::value_type,
+        c10::SymInt>>>
+inline c10::SymInt multiply_integers(Iter begin, Iter end) {
+  return std::accumulate(
+      begin,
+      end,
+      c10::SymInt(1),
+      [](const c10::SymInt& a, const c10::SymInt& b) { return a * b; });
+}
+
+#define DECLARE_SYMINT_OP_INTONLY(scalar_t, RetTy)      \
+  C10_API RetTy operator%(const SymInt& a, scalar_t b); \
+  C10_API RetTy operator%(scalar_t a, const SymInt& b);
+
+#define DECLARE_SYMINT_OP(scalar_t, RetTy)              \
+  C10_API RetTy operator+(const SymInt& a, scalar_t b); \
+  C10_API RetTy operator-(const SymInt& a, scalar_t b); \
+  C10_API RetTy operator*(const SymInt& a, scalar_t b); \
+  C10_API RetTy operator/(const SymInt& a, scalar_t b); \
+  C10_API RetTy operator+(scalar_t a, const SymInt& b); \
+  C10_API RetTy operator-(scalar_t a, const SymInt& b); \
+  C10_API RetTy operator*(scalar_t a, const SymInt& b); \
+  C10_API RetTy operator/(scalar_t a, const SymInt& b); \
+  C10_API bool operator==(const SymInt& a, scalar_t b); \
+  C10_API bool operator!=(const SymInt& a, scalar_t b); \
+  C10_API bool operator<(const SymInt& a, scalar_t b);  \
+  C10_API bool operator<=(const SymInt& a, scalar_t b); \
+  C10_API bool operator>(const SymInt& a, scalar_t b);  \
+  C10_API bool operator>=(const SymInt& a, scalar_t b); \
+  C10_API bool operator==(scalar_t a, const SymInt& b); \
+  C10_API bool operator!=(scalar_t a, const SymInt& b); \
+  C10_API bool operator<(scalar_t a, const SymInt& b);  \
+  C10_API bool operator<=(scalar_t a, const SymInt& b); \
+  C10_API bool operator>(scalar_t a, const SymInt& b);  \
+  C10_API bool operator>=(scalar_t a, const SymInt& b);
+
+DECLARE_SYMINT_OP_INTONLY(int64_t, SymInt)
+DECLARE_SYMINT_OP_INTONLY(int32_t, SymInt)
+DECLARE_SYMINT_OP_INTONLY(uint64_t, SymInt)
+DECLARE_SYMINT_OP_INTONLY(uint32_t, SymInt)
+DECLARE_SYMINT_OP(int64_t, SymInt)
+DECLARE_SYMINT_OP(int32_t, SymInt) // make sure constants work
+DECLARE_SYMINT_OP(uint64_t, SymInt)
+DECLARE_SYMINT_OP(uint32_t, SymInt)
+DECLARE_SYMINT_OP(double, SymFloat)
+DECLARE_SYMINT_OP(float, SymFloat) // just for completeness
+
+// On OSX size_t is different than uint64_t so we have to
+// define it separately
+#if defined(__APPLE__)
+DECLARE_SYMINT_OP_INTONLY(size_t, SymInt)
+DECLARE_SYMINT_OP(size_t, SymInt)
+#endif
+
+#undef DECLARE_SYMINT_OP
+
+C10_API std::ostream& operator<<(std::ostream& os, const SymInt& s);
+C10_API SymInt operator-(const SymInt& s);
+
+inline bool sym_eq(int64_t a, int64_t b) {
+  return a == b;
+}
+
+inline SymBool sym_eq(const SymInt& a, const SymInt& b) {
+  return a.sym_eq(b);
+}
+
+inline bool sym_ne(int64_t a, int64_t b) {
+  return a != b;
+}
+
+inline SymBool sym_ne(const SymInt& a, const SymInt& b) {
+  return a.sym_ne(b);
+}
+
+inline bool sym_lt(int64_t a, int64_t b) {
+  return a < b;
+}
+
+inline SymBool sym_lt(const SymInt& a, const SymInt& b) {
+  return a.sym_lt(b);
+}
+
+inline bool sym_le(int64_t a, int64_t b) {
+  return a <= b;
+}
+
+inline SymBool sym_le(const SymInt& a, const SymInt& b) {
+  return a.sym_le(b);
+}
+
+inline bool sym_gt(int64_t a, int64_t b) {
+  return a > b;
+}
+
+inline SymBool sym_gt(const SymInt& a, const SymInt& b) {
+  return a.sym_gt(b);
+}
+
+inline bool sym_ge(int64_t a, int64_t b) {
+  return a >= b;
+}
+
+inline SymBool sym_ge(const SymInt& a, const SymInt& b) {
+  return a.sym_ge(b);
+}
+
+inline bool definitely_true(
+    const c10::SymBool& b,
+    const char* file,
+    int64_t line) {
+  return b.has_hint() && b.guard_bool(file, line);
+}
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/SymNodeImpl.h

@@ -0,0 +1,236 @@
+#pragma once
+
+#include <c10/macros/Export.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Optional.h>
+#include <c10/util/intrusive_ptr.h>
+#include <cstdint>
+#include <ostream>
+#include <string>
+
+namespace c10 {
+
+class SymNodeImpl;
+using SymNode = c10::intrusive_ptr<SymNodeImpl>;
+
+// When you add a method, you also need to edit
+// torch/csrc/jit/python/init.cpp
+// torch/csrc/utils/python_symnode.h
+// c10/core/ConstantSymNodeImpl.h
+class C10_API SymNodeImpl : public c10::intrusive_ptr_target {
+ public:
+  ~SymNodeImpl() override = default;
+
+  template <typename T>
+  c10::intrusive_ptr<T> dyn_cast() const {
+    return c10::intrusive_ptr<T>::reclaim_copy(dynamic_cast<T*>(this));
+  }
+
+  // these could be pure virtual when we implement LTC versions
+  virtual bool is_int() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual bool is_bool() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual bool is_float() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual bool is_nested_int() const {
+    return false;
+  }
+  virtual SymNode add(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode sub(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode mul(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  // NB: legacy, prefer float_truediv or int_truediv
+  virtual SymNode truediv(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode float_truediv(const SymNode& other) {
+    return truediv(other);
+  }
+  virtual SymNode int_truediv(const SymNode& other) {
+    return truediv(other);
+  }
+  // NB: legacy, prefer float_pow or pow_by_natural
+  virtual SymNode pow(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode float_pow(const SymNode& other) {
+    return pow(other);
+  }
+  virtual SymNode pow_by_natural(const SymNode& other) {
+    return pow(other);
+  }
+  // NB: legacy, prefer int_floordiv
+  virtual SymNode floordiv(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode int_floordiv(const SymNode& other) {
+    return floordiv(other);
+  }
+  virtual SymNode mod(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode eq(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode ne(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode gt(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode lt(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode le(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode ge(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode ceil() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode floor() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode neg() {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual SymNode sym_min(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual SymNode sym_max(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual SymNode sym_or(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual SymNode sym_and(const SymNode& other) {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual SymNode sym_not() {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual SymNode sym_ite(const SymNode& then_val, const SymNode& else_val) {
+    TORCH_CHECK(false, "NYI");
+  };
+  // NB: self is ignored here, only the arguments are used
+  virtual SymNode is_contiguous(
+      ArrayRef<SymNode> sizes,
+      ArrayRef<SymNode> strides) {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual SymNode is_channels_last_contiguous_2d(
+      ArrayRef<SymNode> sizes,
+      ArrayRef<SymNode> strides) {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual SymNode is_channels_last_contiguous_3d(
+      ArrayRef<SymNode> sizes,
+      ArrayRef<SymNode> strides) {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual SymNode is_channels_last_strides_2d(
+      ArrayRef<SymNode> sizes,
+      ArrayRef<SymNode> strides) {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual SymNode is_channels_last_strides_3d(
+      ArrayRef<SymNode> sizes,
+      ArrayRef<SymNode> strides) {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual SymNode is_non_overlapping_and_dense(
+      ArrayRef<SymNode> sizes,
+      ArrayRef<SymNode> strides) {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual SymNode clone() {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual SymNode sym_float() {
+    TORCH_CHECK(false, "NYI");
+  }
+  virtual SymNode wrap_int(int64_t num) {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual SymNode wrap_float(double num) {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual SymNode wrap_bool(bool num) {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual int64_t guard_int(const char* file, int64_t line) {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual bool guard_bool(const char* file, int64_t line) {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual double guard_float(const char* file, int64_t line) {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual bool guard_size_oblivious(const char* file, int64_t line) {
+    // No improvement for unbacked SymBools by default, replace this
+    // with a better implementation!
+    return guard_bool(file, line);
+  }
+  virtual bool expect_true(const char* file, int64_t line) {
+    // No improvement for unbacked SymBools by default, replace this
+    // with a better implementation!
+    return guard_bool(file, line);
+  };
+  virtual bool expect_size(const char* file, int64_t line) {
+    // No improvement for unbacked SymInts by default, replace this
+    // with a better implementation!
+    return ge(wrap_int(0))->guard_bool(file, line);
+  };
+  virtual int64_t int_() {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual bool bool_() {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual bool has_hint() {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual std::string str() {
+    TORCH_CHECK(false, "NYI");
+  };
+  virtual std::optional<int64_t> nested_int() {
+    return c10::nullopt;
+  }
+  virtual std::optional<int64_t> nested_int_coeff() {
+    return c10::nullopt;
+  }
+  virtual std::optional<int64_t> constant_int() {
+    return c10::nullopt;
+  }
+  virtual std::optional<bool> constant_bool() {
+    return c10::nullopt;
+  }
+  virtual std::optional<int64_t> maybe_as_int() {
+    return c10::nullopt;
+  }
+  virtual bool is_constant() {
+    return false;
+  }
+  virtual bool is_symbolic() {
+    return true;
+  }
+  std::ostream& operator<<(std::ostream& os) {
+    os << str();
+    return os;
+  }
+};
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/SymbolicShapeMeta.h

@@ -0,0 +1,214 @@
+#pragma once
+#include <c10/core/SymBool.h>
+#include <c10/core/SymInt.h>
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/DimVector.h>
+
+#include <atomic>
+#include <cstdint>
+#include <mutex>
+#include <utility>
+
+namespace c10 {
+
+class C10_API SymbolicShapeMeta {
+ public:
+  // Basic metadata from which other quantities are derived
+  SymDimVector sizes_ = {0};
+  SymDimVector strides_ = {1};
+  SymInt storage_offset_ = 0;
+
+  bool strides_valid_ = true; // e.g. for sparse where there are no strides
+
+  SymbolicShapeMeta() = default;
+  SymbolicShapeMeta(const SymbolicShapeMeta& other);
+
+  void refresh_numel() {
+    // Non-const, don't need to hold mutables_ lock
+    available_.fetch_and(~numel_avail);
+    numel_ = 1;
+  }
+
+  void refresh_contiguous() {
+    // Non-const, don't need to hold mutables_ lock
+    available_.fetch_and(numel_avail);
+    is_contiguous_ = false;
+    is_channels_last_contiguous_ = false;
+    is_channels_last_3d_contiguous_ = false;
+    is_channels_last_ = false;
+    is_channels_last_3d_ = false;
+    is_non_overlapping_and_dense_ = false;
+  }
+
+  int64_t dim() const {
+    return static_cast<int64_t>(sizes_.size());
+  }
+
+  // Accessors for derived quantities, computed lazily on first access
+
+  bool has_numel() const {
+    return available_.load() & numel_avail;
+  }
+  bool has_is_contiguous() const {
+    return available_.load() & is_contiguous_avail;
+  }
+  bool has_is_channels_last_contiguous() const {
+    return available_.load() & is_channels_last_contiguous_avail;
+  }
+  bool has_is_channels_last_3d_contiguous() const {
+    return available_.load() & is_channels_last_3d_contiguous_avail;
+  }
+  bool has_is_channels_last() const {
+    return available_.load() & is_channels_last_avail;
+  }
+  bool has_is_channels_last_3d() const {
+    return available_.load() & is_channels_last_3d_avail;
+  }
+  bool has_is_non_overlapping_and_dense() const {
+    return available_.load() & is_non_overlapping_and_dense_avail;
+  }
+
+  // Accessors to cached derived properties
+  // DO NOT call with mutables_ lock held
+  const SymInt& numel() const {
+    if (C10_UNLIKELY(!has_numel())) {
+      init_numel();
+    }
+    return numel_;
+  }
+
+  const SymBool& is_contiguous() const {
+    if (C10_UNLIKELY(!has_is_contiguous())) {
+      init_is_contiguous();
+    }
+    return is_contiguous_;
+  }
+
+  const SymBool& is_channels_last_contiguous() const {
+    if (C10_UNLIKELY(!has_is_channels_last_contiguous())) {
+      init_is_channels_last_contiguous();
+    }
+    return is_channels_last_contiguous_;
+  }
+
+  const SymBool& is_channels_last_3d_contiguous() const {
+    if (C10_UNLIKELY(!has_is_channels_last_3d_contiguous())) {
+      init_is_channels_last_3d_contiguous();
+    }
+    return is_channels_last_3d_contiguous_;
+  }
+
+  const SymBool& is_channels_last() const {
+    if (C10_UNLIKELY(!has_is_channels_last())) {
+      init_is_channels_last();
+    }
+    return is_channels_last_;
+  }
+
+  const SymBool& is_channels_last_3d() const {
+    if (C10_UNLIKELY(!has_is_channels_last_3d())) {
+      init_is_channels_last_3d();
+    }
+    return is_channels_last_3d_;
+  }
+
+  const SymBool& is_non_overlapping_and_dense() const {
+    if (C10_UNLIKELY(!has_is_non_overlapping_and_dense())) {
+      init_is_non_overlapping_and_dense();
+    }
+    return is_non_overlapping_and_dense_;
+  }
+
+  // Assumptions so we can short-circuit computation
+  // NOTE: Don't need to lock mutables_ since these aren't const
+  void assume_contiguous(SymBool val = true) {
+    is_contiguous_ = std::move(val);
+    available_.fetch_or(is_contiguous_avail);
+  }
+  void assume_channels_last_contiguous(SymBool val = true) {
+    is_contiguous_ = std::move(val);
+    available_.fetch_or(is_channels_last_contiguous_avail);
+  }
+  void assume_channels_last_3d_contiguous(SymBool val = true) {
+    is_channels_last_3d_contiguous_ = std::move(val);
+    available_.fetch_or(is_channels_last_3d_contiguous_avail);
+  }
+  void assume_channels_last(SymBool val = true) {
+    is_channels_last_ = std::move(val);
+    available_.fetch_or(is_channels_last_avail);
+  }
+  void assume_channels_last_3d(SymBool val = true) {
+    is_channels_last_3d_ = std::move(val);
+    available_.fetch_or(is_channels_last_3d_avail);
+  }
+  void assume_non_overlapping_and_dense(SymBool val = true) {
+    is_non_overlapping_and_dense_ = std::move(val);
+    available_.fetch_or(is_non_overlapping_and_dense_avail);
+  }
+
+ private:
+  SymBool compute_contiguous() const;
+  SymBool compute_channels_last_contiguous_2d() const;
+  SymBool compute_channels_last_contiguous_3d() const;
+  SymBool compute_strides_like_channels_last_2d() const;
+  SymBool compute_strides_like_channels_last_3d() const;
+  SymBool compute_non_overlapping_and_dense() const;
+
+  // These are little wrappers over the real compute_ functions that
+  // can make use of other contiguity fields to short circuit.
+  // They need to be implemented separately for SymBool, as SymBool does
+  // not short circuit.
+  // TODO: should the SymBool cases avoid the short circuit?  Need to reason
+  // if its correct, and reason if the simpler expressions are better for
+  // analysis (maybe not!)
+
+  SymBool compute_channels_last_contiguous_3d_dim5() const;
+  SymBool compute_channels_last_2d_dim5() const;
+  SymBool compute_channels_last_3d_dim5() const;
+  SymBool compute_is_non_overlapping_and_dense_dim4() const;
+  SymBool compute_is_non_overlapping_and_dense_dim5() const;
+  SymBool compute_is_non_overlapping_and_dense_anydim() const;
+
+  void init_numel() const;
+  void init_is_contiguous() const;
+  void init_is_channels_last_contiguous() const;
+  void init_is_channels_last_3d_contiguous() const;
+  void init_is_channels_last() const;
+  void init_is_channels_last_3d() const;
+  void init_is_non_overlapping_and_dense() const;
+
+  // NOTE: These only set if !has_foo()
+  void set_numel(SymInt val) const;
+  void set_is_contiguous(SymBool val) const;
+  void set_is_channels_last_contiguous(SymBool val) const;
+  void set_is_channels_last_3d_contiguous(SymBool val) const;
+  void set_is_channels_last(SymBool val) const;
+  void set_is_channels_last_3d(SymBool val) const;
+  void set_is_non_overlapping_and_dense(SymBool val) const;
+
+  // Lazily initialized variables, with the corresponding available_ flag
+  // indicating whether the value has been initialized
+  mutable std::atomic<int> available_{0};
+  enum avail {
+    numel_avail = 1 << 0,
+    is_contiguous_avail = 1 << 1,
+    is_channels_last_contiguous_avail = 1 << 2,
+    is_channels_last_3d_contiguous_avail = 1 << 3,
+    is_channels_last_avail = 1 << 4,
+    is_channels_last_3d_avail = 1 << 5,
+    is_non_overlapping_and_dense_avail = 1 << 6,
+  };
+
+  // Mutex to prevent races when initializing the variable from const accessors
+  mutable std::mutex mutables_;
+  mutable SymInt numel_ = 1;
+  mutable SymBool is_contiguous_{true};
+  mutable SymBool is_channels_last_contiguous_{false};
+  mutable SymBool is_channels_last_3d_contiguous_{false};
+  mutable SymBool is_channels_last_{false};
+  mutable SymBool is_channels_last_3d_{false};
+  mutable SymBool is_non_overlapping_and_dense_{true};
+};
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/TensorOptions.h

@@ -0,0 +1,787 @@
+#pragma once
+
+#include <c10/core/Backend.h>
+#include <c10/core/DefaultDtype.h>
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/core/DispatchKey.h>
+#include <c10/core/Layout.h>
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/ScalarType.h>
+#include <c10/core/ScalarTypeToTypeMeta.h>
+
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Optional.h>
+
+#include <cstdint>
+#include <iosfwd>
+#include <string>
+#include <type_traits>
+#include <utility>
+
+namespace c10 {
+
+DispatchKey computeDispatchKey(
+    std::optional<ScalarType> dtype,
+    std::optional<Layout> layout,
+    std::optional<Device> device);
+
+inline ScalarType dtype_or_default(std::optional<ScalarType> dtype) {
+  return value_or_else(dtype, [] { return get_default_dtype_as_scalartype(); });
+}
+
+inline caffe2::TypeMeta dtype_or_default(
+    std::optional<caffe2::TypeMeta> dtype) {
+  return value_or_else(dtype, [] { return get_default_dtype(); });
+}
+
+inline Layout layout_or_default(std::optional<Layout> layout) {
+  return layout.value_or(kStrided);
+}
+
+inline Device device_or_default(std::optional<Device> device) {
+  return value_or_else(device, [] { return Device(kCPU); });
+}
+
+inline bool pinned_memory_or_default(std::optional<bool> pinned_memory) {
+  return pinned_memory.value_or(false);
+}
+
+/// A class to encapsulate construction axes of an Tensor.  TensorOptions was
+/// designed to support the Python style API for specifying construction options
+/// on factory functions, e.g.,
+///
+///     torch.zeros(2, 3, dtype=torch.int32)
+///
+/// Because C++ doesn't natively support keyword arguments, there must be
+/// another way of specifying keyword-like arguments.  TensorOptions is a
+/// builder class which can be used to construct this "dictionary" of keyword
+/// arguments: functions which support TensorOptions conventionally take this
+/// argument optionally as their last argument.
+///
+/// WARNING: In PyTorch, there are `torch::` variants of factory functions,
+/// e.g., torch::zeros for at::zeros.  These return Variables (while the
+/// stock ATen functions return plain Tensors).  If you mix these functions
+/// up, you WILL BE SAD.
+///
+/// Rather than use the constructor of this class directly, you should prefer to
+/// use the constructor functions, and then chain setter methods on top of them.
+///
+///     at::device(at::kCUDA).dtype(kInt)
+///     at::dtype(at::kInt)
+///
+/// Additionally, anywhere a TensorOptions is expected, you can directly
+/// pass at::kCUDA / at::kInt, and it will implicitly convert to a
+/// TensorOptions.
+///
+/// Here are some recommended ways to create a 2x2 tensor of zeros
+/// with certain properties.  These all *implicitly* make use of
+/// TensorOptions, even if they don't mention the class explicitly:
+///
+///     at::zeros({2,2}, at::kCUDA);
+///     at::zeros({2,2}, at::kLong);
+///     at::zeros({2,2}, at::device(at::kCUDA).dtype(at::kLong()));
+///     at::zeros({2,2}, at::device({at::kCUDA, 1})); // place on device 1
+///     at::zeros({2,2}, at::requires_grad());
+///
+
+/// NOTE [ TensorOptions Constructors ]
+///
+/// TensorOptions is like a dictionary with entries from the set:
+/// {requires_grad, device, dtype, layout}, where each entry may be
+/// unspecified (i.e., is optional). It is used to specify the properties of
+/// tensors in many places both in C++ internal and API, e.g., tensor factory
+/// methods like `at::empty({10}, options)`, tensor conversions like
+/// `tensor.to(...)`, etc.
+///
+/// To provide a simple API that is consistent with Python, where one can do
+/// `torch.empty(sizes, X)` with `X` being a `torch.device`, `torch.dtype`, or a
+/// `torch.layout`, we want TensorOptions to be implicitly convertible from
+/// `ScalarType dtype`, `Layout layout` and `Device device`. Therefore, we have
+/// three implicit constructors from each of these three types.
+///
+/// This is sufficient for `ScalarType` and `Layout` as they are simple Enum
+/// classes. However, `Device` is an ordinary class with implicit constructors
+/// `Device(DeviceType, DeviceIndex = -1)` and `Device(std::string)` to be
+/// consistent with Python API, where strings are treated as equivalent with a
+/// `torch.device` object (e.g., "cuda:1" can be passed to everywhere a
+/// `torch.device("cuda:1")` is accepted). To support the syntax
+/// `at::empty({10}, {kCUDA, 1})` and `tensor.to(kCUDA)`, we need to make sure
+/// that `TensorOptions` is implicitly constructible with any arguments that a
+/// `Device` can constructed from. So we have,
+///
+///    /* implicit */ TensorOptions(T&& device) : TensorOptions() {
+///      this->set_device(device);
+///    }
+///
+///    template <typename... Args,
+///             typename = std::enable_if_t<std::is_constructible<Device,
+///             Args&&...>::value>>
+///    /* implicit */  TensorOptions(Args&&... args)
+///     : TensorOptions(Device(std::forward<Args>(args)...)) {}
+///
+///
+/// But this will be problematic. Consider this: `TensorOptions({kCUDA, 1})`.
+/// Compiler will complain about ambiguity between the copy constructor and the
+/// `Device` constructor because `{kCUDA, 1}` can be converted to both a
+/// `TensorOption` and a `Device`.
+///
+/// To get around this, we templatize the `Device` constructor. Since overload
+/// resolution is done before template resolution, our problem is solved.
+
+DispatchKey computeDispatchKey(
+    optional<ScalarType> dtype,
+    optional<Layout> layout,
+    optional<Device> device);
+
+struct C10_API TensorOptions {
+  TensorOptions()
+      : requires_grad_(false),
+        pinned_memory_(false),
+        has_device_(false),
+        has_dtype_(false),
+        has_layout_(false),
+        has_requires_grad_(false),
+        has_pinned_memory_(false),
+        has_memory_format_(false) {}
+
+  /// Constructs a `TensorOptions` object with the given layout.
+  /* implicit */ TensorOptions(Layout layout) : TensorOptions() {
+    this->set_layout(layout);
+  }
+
+  /// Constructs a `TensorOptions` object with the given device.
+  /// See NOTE [ TensorOptions Constructors ] on why this is templatized.
+  template <
+      typename T,
+      typename = std::enable_if_t<std::is_same_v<std::decay_t<T>, Device>>>
+  /* implicit */ TensorOptions(T&& device) : TensorOptions() {
+    this->set_device(std::forward<T>(device));
+  }
+
+  /// Constructs a `TensorOptions` object from arguments allowed in `Device`
+  /// constructors.
+  ///
+  /// See NOTE [ TensorOptions Constructors ].
+  ///
+  /// NB: Ideally we only allow implicit constructors here. But there is no easy
+  ///     way to detect them. So we have this one that allows explicit
+  ///     constructors too.
+  template <
+      typename... Args,
+      typename = std::enable_if_t<std::is_constructible_v<Device, Args&&...>>>
+  /* implicit */ TensorOptions(Args&&... args)
+      : TensorOptions(Device(std::forward<Args>(args)...)) {}
+
+  /// Constructs a `TensorOptions` object with the given dtype.
+  /* implicit */ TensorOptions(caffe2::TypeMeta dtype) : TensorOptions() {
+    this->set_dtype(dtype);
+  }
+
+  /// legacy constructor to support ScalarType
+  /* implicit */ TensorOptions(ScalarType dtype) : TensorOptions() {
+    this->set_dtype(dtype);
+  }
+
+  /// Constructs a `TensorOptions` object with the given memory format.
+  /* implicit */ TensorOptions(MemoryFormat memory_format) : TensorOptions() {
+    set_memory_format(memory_format);
+  }
+
+  /// Return a copy of `TensorOptions` with `device` set to the given one, or
+  /// cleared if `device` is `nullopt`.
+  C10_NODISCARD TensorOptions
+  device(std::optional<Device> device) const noexcept {
+    TensorOptions r = *this;
+    r.set_device(device);
+    return r;
+  }
+
+  /// Return a copy of `TensorOptions` with `device` set to the given one.
+  /// (This overload ensures that variadic template std::optional constructor
+  /// for Device work correctly.)
+  template <typename... Args>
+  C10_NODISCARD TensorOptions device(Args&&... args) const noexcept {
+    return device(
+        std::optional<Device>(std::in_place, std::forward<Args>(args)...));
+  }
+
+  /// Return a copy of `TensorOptions`, but with device set to CUDA, and the
+  /// device index set to the given one.
+  ///
+  /// TODO: This function encourages bad behavior (assuming CUDA is
+  /// the only device that matters).  Get rid of it / rename it.
+  C10_NODISCARD TensorOptions
+  device_index(c10::DeviceIndex device_index) const noexcept {
+    return device(Device::Type::CUDA, device_index);
+  }
+
+  /// Return a copy of `TensorOptions` with `dtype` set to the given one.
+  C10_NODISCARD TensorOptions
+  dtype(std::optional<caffe2::TypeMeta> dtype) const noexcept {
+    TensorOptions r = *this;
+    r.set_dtype(dtype);
+    return r;
+  }
+
+  // legacy function to support ScalarType
+  C10_NODISCARD TensorOptions
+  dtype(std::optional<ScalarType> dtype) const noexcept {
+    TensorOptions r = *this;
+    r.set_dtype(dtype);
+    return r;
+  }
+
+  // Since dtype is taken...
+  template <typename T>
+  TensorOptions& dtype() {
+    dtype_ = caffe2::TypeMeta::Make<T>();
+    has_dtype_ = true;
+    return *this;
+  }
+
+  /// Sets the layout of the `TensorOptions`.
+  C10_NODISCARD TensorOptions
+  layout(std::optional<Layout> layout) const noexcept {
+    TensorOptions r = *this;
+    r.set_layout(layout);
+    return r;
+  }
+
+  /// Sets the `requires_grad` property of the `TensorOptions`.
+  C10_NODISCARD TensorOptions
+  requires_grad(std::optional<bool> requires_grad) const noexcept {
+    TensorOptions r = *this;
+    r.set_requires_grad(requires_grad);
+    return r;
+  }
+
+  /// Sets the `pinned_memory` property on the `TensorOptions`.
+  C10_NODISCARD TensorOptions
+  pinned_memory(std::optional<bool> pinned_memory) const noexcept {
+    TensorOptions r = *this;
+    r.set_pinned_memory(pinned_memory);
+    return r;
+  }
+
+  /// Sets the `memory_format` property on `TensorOptions`.
+  C10_NODISCARD TensorOptions
+  memory_format(std::optional<MemoryFormat> memory_format) const noexcept {
+    TensorOptions r = *this;
+    r.set_memory_format(memory_format);
+    return r;
+  }
+
+  /// Returns the device of the `TensorOptions`.
+  Device device() const noexcept {
+    return device_or_default(device_opt());
+  }
+
+  /// Returns whether the device is specified.
+  bool has_device() const noexcept {
+    return has_device_;
+  }
+
+  /// Returns the device of the `TensorOptions`, or `c10::nullopt` if
+  /// device is not specified.
+  std::optional<Device> device_opt() const noexcept {
+    return has_device_ ? c10::make_optional(device_) : c10::nullopt;
+  }
+
+  /// Returns the device index of the `TensorOptions`.
+  c10::DeviceIndex device_index() const noexcept {
+    return device().index();
+  }
+
+  /// Returns the dtype of the `TensorOptions`.
+  caffe2::TypeMeta dtype() const noexcept {
+    return dtype_or_default(dtype_opt());
+  }
+
+  /// Returns whether the dtype is specified.
+  bool has_dtype() const noexcept {
+    return has_dtype_;
+  }
+
+  /// Returns the dtype of the `TensorOptions`, or `c10::nullopt` if
+  /// device is not specified.
+  std::optional<caffe2::TypeMeta> dtype_opt() const noexcept {
+    return has_dtype_ ? c10::make_optional(dtype_) : c10::nullopt;
+  }
+
+  /// Returns the layout of the `TensorOptions`.
+  Layout layout() const noexcept {
+    return layout_or_default(layout_opt());
+  }
+
+  /// Returns whether the layout is specified.
+  bool has_layout() const noexcept {
+    return has_layout_;
+  }
+
+  /// Returns the layout of the `TensorOptions`, or `c10::nullopt` if
+  /// layout is not specified.
+  std::optional<Layout> layout_opt() const noexcept {
+    return has_layout_ ? c10::make_optional(layout_) : c10::nullopt;
+  }
+
+  /// Returns the `requires_grad` property of the `TensorOptions`.
+  bool requires_grad() const noexcept {
+    return has_requires_grad_ ? requires_grad_ : false;
+  }
+
+  /// Returns whether the `requires_grad` is specified.
+  bool has_requires_grad() const noexcept {
+    return has_requires_grad_;
+  }
+
+  /// Returns the `requires_grad` property of the `TensorOptions`, or
+  /// `c10::nullopt` if `requires_grad` is not specified.
+  std::optional<bool> requires_grad_opt() const noexcept {
+    return has_requires_grad_ ? c10::make_optional(requires_grad_)
+                              : c10::nullopt;
+  }
+
+  /// Returns the `pinned_memory` property of the `TensorOptions`.
+  bool pinned_memory() const noexcept {
+    return pinned_memory_or_default(pinned_memory_opt());
+  }
+
+  /// Returns whether the `pinned_memory` is specified.
+  bool has_pinned_memory() const noexcept {
+    return has_pinned_memory_;
+  }
+
+  /// Returns if the layout is sparse
+  bool is_sparse() const {
+    return layout_ == c10::Layout::Sparse;
+  }
+
+  /// Returns if the layout is sparse CSR, deprecated, use
+  /// is_sparse_compressed() instead
+  bool is_sparse_csr() const {
+    return layout_ == c10::Layout::SparseCsr;
+  }
+
+  bool is_sparse_compressed() const {
+    return layout_ == c10::Layout::SparseCsr ||
+        layout_ == c10::Layout::SparseCsc ||
+        layout_ == c10::Layout::SparseBsr || layout_ == c10::Layout::SparseBsc;
+  }
+
+  // For compatibility with legacy tensor.type() comparisons
+  bool type_equal(const TensorOptions& other) const {
+    return computeDispatchKey() == other.computeDispatchKey() &&
+        typeMetaToScalarType(dtype_) == typeMetaToScalarType(other.dtype());
+  }
+
+  /// Returns the `pinned_memory` property of the `TensorOptions`, or
+  /// `c10::nullopt` if `pinned_memory` is not specified.
+  std::optional<bool> pinned_memory_opt() const noexcept {
+    return has_pinned_memory_ ? c10::make_optional(pinned_memory_)
+                              : c10::nullopt;
+  }
+
+  /// Returns whether the `memory_layout` is specified
+  bool has_memory_format() const noexcept {
+    return has_memory_format_;
+  }
+
+  // NB: memory_format() getter is PURPOSELY not defined, as the default
+  // behavior of memory_format varies from function to function.
+
+  /// Returns the `memory_layout` property of `TensorOptions, or
+  /// `c10::nullopt` if `memory_format` is not specified.
+  std::optional<MemoryFormat> memory_format_opt() const noexcept {
+    return has_memory_format_ ? c10::make_optional(memory_format_)
+                              : c10::nullopt;
+  }
+
+  // Resolves the ATen backend specified by the current construction axes.
+  // TODO: Deprecate this
+  Backend backend() const {
+    return at::dispatchKeyToBackend(computeDispatchKey());
+  }
+
+  /// Return the right-biased merge of two TensorOptions.  This has the
+  /// effect of overwriting settings from self with specified options
+  /// of options.
+  ///
+  /// NB: This merging operation does NOT respect device merges.
+  /// For example, if you device({kCUDA, 1}).merge_in(kCUDA)
+  /// you will get kCUDA in the end!  Functions like Tensor.new_empty
+  /// ensure the right device is selected anyway by way of a
+  /// device guard.
+  ///
+  TensorOptions merge_in(TensorOptions options) const noexcept {
+    TensorOptions merged = *this;
+    if (options.has_device())
+      merged.set_device(options.device_opt());
+    if (options.has_dtype())
+      merged.set_dtype(options.dtype_opt());
+    if (options.has_layout())
+      merged.set_layout(options.layout_opt());
+    // NB: requires grad is right biased; not a logical AND/OR!
+    if (options.has_requires_grad())
+      merged.set_requires_grad(options.requires_grad_opt());
+    if (options.has_pinned_memory())
+      merged.set_pinned_memory(options.pinned_memory_opt());
+    if (options.has_memory_format())
+      merged.set_memory_format(options.memory_format_opt());
+    return merged;
+  }
+
+  // TODO remove after TensorOptions rationalization
+  TensorOptions merge_memory_format(
+      std::optional<MemoryFormat> optional_memory_format) const noexcept {
+    TensorOptions merged = *this;
+    if (optional_memory_format.has_value()) {
+      merged.set_memory_format(*optional_memory_format);
+    }
+    return merged;
+  }
+
+  // INVARIANT: computeDispatchKey returns only the subset of dispatch keys for
+  // which dispatchKeyToBackend is injective, if it is defined at all  (for
+  // the most part, this just means that this function never returns an
+  // Autograd key)
+  DispatchKey computeDispatchKey() const {
+    return c10::computeDispatchKey(
+        optTypeMetaToScalarType(dtype_opt()), layout_opt(), device_opt());
+  }
+
+ private:
+  // These methods are currently private because I'm not sure if it's wise
+  // to actually publish them.  They are methods because I need them in
+  // the constructor and the functional API implementation.
+  //
+  // If you really, really need it, you can make these public, but check if you
+  // couldn't just do what you need with the functional API.  Similarly, these
+  // methods are not chainable, because if you wanted chaining, you probably
+  // want to use the functional API instead.  (It's probably OK to make
+  // these chainable, because these functions are all explicitly annotated
+  // with a ref-qualifier, the trailing &, that makes them illegal to call
+  // on temporaries.)
+
+  /// Mutably set the device of `TensorOptions`.
+  void set_device(std::optional<Device> device) & noexcept {
+    if (device) {
+      device_ = *device;
+      has_device_ = true;
+    } else {
+      has_device_ = false;
+    }
+  }
+
+  /// Mutably set the dtype of `TensorOptions`.
+  void set_dtype(std::optional<caffe2::TypeMeta> dtype) & noexcept {
+    if (dtype) {
+      dtype_ = *dtype;
+      has_dtype_ = true;
+    } else {
+      has_dtype_ = false;
+    }
+  }
+
+  // legacy function to support ScalarType
+  void set_dtype(std::optional<ScalarType> dtype) & noexcept {
+    if (dtype) {
+      dtype_ = scalarTypeToTypeMeta(*dtype);
+      has_dtype_ = true;
+    } else {
+      has_dtype_ = false;
+    }
+  }
+
+  /// Mutably set the layout of `TensorOptions`.
+  void set_layout(std::optional<Layout> layout) & noexcept {
+    if (layout) {
+      layout_ = *layout;
+      has_layout_ = true;
+    } else {
+      has_layout_ = false;
+    }
+  }
+
+  /// Mutably set the `requires_grad` property of `TensorOptions`.
+  void set_requires_grad(std::optional<bool> requires_grad) & noexcept {
+    if (requires_grad) {
+      requires_grad_ = *requires_grad;
+      has_requires_grad_ = true;
+    } else {
+      has_requires_grad_ = false;
+    }
+  }
+
+  /// Mutably set the `pinned_memory` property of `TensorOptions`.
+  void set_pinned_memory(std::optional<bool> pinned_memory) & noexcept {
+    if (pinned_memory) {
+      pinned_memory_ = *pinned_memory;
+      has_pinned_memory_ = true;
+    } else {
+      has_pinned_memory_ = false;
+    }
+  }
+
+  /// Mutably set the `memory_Format` property of `TensorOptions`.
+  void set_memory_format(std::optional<MemoryFormat> memory_format) & noexcept {
+    if (memory_format) {
+      memory_format_ = *memory_format;
+      has_memory_format_ = true;
+    } else {
+      has_memory_format_ = false;
+    }
+  }
+
+  // WARNING: If you edit TensorOptions to add more options, you
+  // may need to adjust the implementation of Tensor::options.
+  // The criteria for whether or not Tensor::options must be adjusted
+  // is whether or not the new option you added should preserved
+  // by functions such as empty_like(); if it should be preserved,
+  // you must adjust options().
+  //
+  // TODO: MemoryFormat is not implemented in this way
+
+  // NB: We didn't use std::optional here, because then we can't pack
+  // the has_***_ boolean fields.
+
+  Device device_ = at::kCPU; // 16-bit
+  caffe2::TypeMeta dtype_ = caffe2::TypeMeta::Make<float>(); // 16-bit
+  Layout layout_ = at::kStrided; // 8-bit
+  MemoryFormat memory_format_ = MemoryFormat::Contiguous; // 8-bit
+
+  // Bitmask required here to get this to fit inside 32 bits (or even 64 bits,
+  // for that matter)
+
+  bool requires_grad_ : 1;
+  bool pinned_memory_ : 1;
+
+  bool has_device_ : 1;
+  bool has_dtype_ : 1;
+  bool has_layout_ : 1;
+  bool has_requires_grad_ : 1;
+  bool has_pinned_memory_ : 1;
+  bool has_memory_format_ : 1;
+};
+
+// We should aspire to fit in one machine-size word; but a size greater than two
+// words is too much.  (We are doing terribly on 32-bit archs, where we require
+// three machine size words to store tensor options.  Eek!)
+static_assert(
+    sizeof(TensorOptions) <= sizeof(int64_t) * 2,
+    "TensorOptions must fit in 128-bits");
+
+/// Convenience function that returns a `TensorOptions` object with the `dtype`
+/// set to the given one.
+inline TensorOptions dtype(caffe2::TypeMeta dtype) {
+  return TensorOptions().dtype(dtype);
+}
+
+// legacy function to support ScalarType
+inline TensorOptions dtype(ScalarType dtype) {
+  return TensorOptions().dtype(scalarTypeToTypeMeta(dtype));
+}
+
+/// Convenience function that returns a `TensorOptions` object with the `layout`
+/// set to the given one.
+inline TensorOptions layout(Layout layout) {
+  return TensorOptions().layout(layout);
+}
+
+/// Convenience function that returns a `TensorOptions` object with the `device`
+/// set to the given one.
+inline TensorOptions device(Device device) {
+  return TensorOptions().device(device);
+}
+
+/// Convenience function that returns a `TensorOptions` object with the
+/// `device` set to CUDA and the `device_index` set to the given one.
+inline TensorOptions device_index(c10::DeviceIndex device_index) {
+  return TensorOptions().device_index(device_index);
+}
+
+/// Convenience function that returns a `TensorOptions` object with the
+/// `requires_grad` set to the given one.
+inline TensorOptions requires_grad(bool requires_grad = true) {
+  return TensorOptions().requires_grad(requires_grad);
+}
+
+/// Convenience function that returns a `TensorOptions` object with the
+/// `memory_format` set to the given one.
+inline TensorOptions memory_format(MemoryFormat memory_format) {
+  return TensorOptions().memory_format(memory_format);
+}
+
+C10_API std::ostream& operator<<(
+    std::ostream& stream,
+    const TensorOptions& options);
+
+template <typename T>
+inline TensorOptions dtype() {
+  return dtype(caffe2::TypeMeta::Make<T>());
+}
+
+inline std::string toString(const TensorOptions& options) {
+  std::ostringstream stream;
+  stream << options;
+  return stream.str();
+}
+
+// This is intended to be a centralized location by which we can determine
+// what an appropriate DispatchKey for a tensor is.
+inline DispatchKey computeDispatchKey(
+    std::optional<ScalarType> dtype,
+    std::optional<Layout> layout,
+    std::optional<Device> device) {
+  const auto layout_ = layout_or_default(layout);
+  const auto device_ = device_or_default(device);
+  switch (layout_) {
+    case Layout::Jagged:
+    case Layout::Strided: {
+      const auto dtype_ = dtype_or_default(dtype);
+      switch (device_.type()) {
+#define DO_CASE(device, _)                   \
+  case c10::DeviceType::device: {            \
+    if (isQIntType(dtype_)) {                \
+      return DispatchKey::Quantized##device; \
+    }                                        \
+    return DispatchKey::device;              \
+  }
+        C10_FORALL_BACKEND_DEVICE_TYPES(DO_CASE, unused)
+#undef DO_CASE
+        case c10::DeviceType::FPGA:
+          return DispatchKey::FPGA;
+        case c10::DeviceType::MAIA:
+          return DispatchKey::MAIA;
+        case c10::DeviceType::Vulkan:
+          return DispatchKey::Vulkan;
+        case c10::DeviceType::Metal:
+          return DispatchKey::Metal;
+        case c10::DeviceType::MKLDNN:
+        case c10::DeviceType::OPENGL:
+        case c10::DeviceType::OPENCL:
+        case c10::DeviceType::IDEEP:
+          TORCH_INTERNAL_ASSERT(
+              0,
+              "This is a grandfathered Caffe2 device type ",
+              device_.type(),
+              ", it shouldn't ever convert to a DispatchKey.  File a bug describing what you were doing if you think this is in error.");
+        default:
+          TORCH_CHECK_NOT_IMPLEMENTED(
+              false,
+              "Unsupported device type for dense layout: ",
+              device_.type());
+      }
+    }
+    case Layout::Sparse:
+      switch (device_.type()) {
+#define DO_CASE(device, _)              \
+  case c10::DeviceType::device: {       \
+    return DispatchKey::Sparse##device; \
+  }
+        C10_FORALL_BACKEND_DEVICE_TYPES(DO_CASE, unused)
+#undef DO_CASE
+        default:
+          TORCH_CHECK_NOT_IMPLEMENTED(
+              false,
+              "Unsupported device type for sparse layout: ",
+              device_.type());
+      }
+    case Layout::Mkldnn:
+      switch (device_.type()) {
+        case c10::DeviceType::CPU:
+          return DispatchKey::MkldnnCPU;
+        default:
+          TORCH_CHECK_NOT_IMPLEMENTED(
+              false,
+              "Unsupported device type for mkldnn layout: ",
+              device_.type());
+      }
+    case Layout::SparseCsr:
+    case Layout::SparseCsc:
+    case Layout::SparseBsr:
+    case Layout::SparseBsc:
+      switch (device_.type()) {
+#define DO_CASE(device, _)                 \
+  case c10::DeviceType::device: {          \
+    return DispatchKey::SparseCsr##device; \
+  }
+        C10_FORALL_BACKEND_DEVICE_TYPES(DO_CASE, unused)
+#undef DO_CASE
+        default:
+          TORCH_CHECK_NOT_IMPLEMENTED(
+              false,
+              "Unsupported device type for ",
+              layout_,
+              " layout: ",
+              device_.type());
+      }
+    default:
+      TORCH_CHECK(false, "Unsupported layout: ", layout_);
+  }
+}
+
+inline Layout dispatchKeyToLayout(DispatchKey dispatch_key) {
+  switch (dispatch_key) {
+#define DO_CASE(bc, _) case DispatchKey::Sparse##bc:
+    C10_FORALL_BACKEND_COMPONENTS(DO_CASE, unused)
+#undef DO_CASE
+    return Layout::Sparse;
+#define DO_CASE(bc, _) case DispatchKey::SparseCsr##bc:
+    C10_FORALL_BACKEND_COMPONENTS(DO_CASE, unused)
+#undef DO_CASE
+    TORCH_CHECK(
+        false, "Cannot map DispatchKey ", dispatch_key, " to a unique layout.");
+    case DispatchKey::MkldnnCPU:
+      return Layout::Mkldnn;
+    default:
+      return Layout::Strided;
+  }
+}
+
+inline c10::DeviceType dispatchKeyToDeviceType(DispatchKey dispatch_key) {
+  switch (dispatch_key) {
+    // stuff that's real
+#define DO_CASE(suffix, prefix)     \
+  case DispatchKey::prefix##suffix: \
+    return c10::DeviceType::suffix;
+#define DO_CASES(_, prefix) C10_FORALL_BACKEND_DEVICE_TYPES(DO_CASE, prefix)
+    C10_FORALL_FUNCTIONALITY_KEYS(DO_CASES)
+#undef DO_CASES
+#undef DO_CASE
+
+    case DispatchKey::MkldnnCPU:
+      return c10::DeviceType::CPU;
+    case DispatchKey::Vulkan:
+      return c10::DeviceType::Vulkan;
+
+    case DispatchKey::MAIA:
+      return c10::DeviceType::MAIA;
+    default:
+      TORCH_CHECK(
+          false,
+          "DispatchKey ",
+          dispatch_key,
+          " doesn't correspond to a device");
+  }
+}
+
+inline TensorOptions dispatchKeyToTensorOptions(DispatchKey dispatch_key) {
+  return TensorOptions()
+      .layout(dispatchKeyToLayout(dispatch_key))
+      .device(dispatchKeyToDeviceType(dispatch_key));
+}
+
+namespace detail {
+inline bool backend_supports_empty_operator(const TensorOptions& options) {
+  // Quantized backends don't support at::empty().
+  // They have separate operators like at::empty_quantized() that take in
+  // extra information about how to quantize the tensor.
+  return !isQIntType(typeMetaToScalarType(options.dtype()));
+}
+
+} // namespace detail
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/UndefinedTensorImpl.h

@@ -0,0 +1,42 @@
+#pragma once
+
+#include <c10/core/MemoryFormat.h>
+#include <c10/core/SymIntArrayRef.h>
+#include <c10/core/TensorImpl.h>
+#include <c10/macros/Export.h>
+#include <c10/util/ArrayRef.h>
+#include <cstdint>
+
+namespace c10 {
+
+struct C10_API UndefinedTensorImpl final : public TensorImpl {
+ public:
+  // Without this, we get:
+  //  error: identifier "at::UndefinedTensorImpl::_singleton" is undefined in
+  //  device code
+  // (ostensibly because the constexpr tricks MSVC into trying to compile this
+  // function for device as well).
+#ifdef _WIN32
+  static inline TensorImpl* singleton() {
+#else
+  static constexpr inline TensorImpl* singleton() {
+#endif
+    return &_singleton;
+  }
+#ifdef DEBUG
+  bool has_storage() const override;
+#endif
+  void set_storage_offset(int64_t offset) override;
+
+ protected:
+  bool is_contiguous_custom(MemoryFormat format) const override;
+  IntArrayRef strides_custom() const override;
+  SymIntArrayRef sym_strides_custom() const override;
+
+ private:
+  UndefinedTensorImpl();
+  static UndefinedTensorImpl _singleton;
+  const char* tensorimpl_type_name() const override;
+};
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/WrapDimMinimal.h

@@ -0,0 +1,48 @@
+#pragma once
+
+#include <c10/core/SymInt.h>
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <cstdint>
+#include <utility>
+
+namespace c10 {
+
+namespace detail {
+// This template can only be specialized at int64_t and c10::SymInt;
+// you'll get linker errors otherwise
+template <typename T>
+C10_API T maybe_wrap_dim_slow(T dim, T dim_post_expr, bool wrap_scalar);
+} // namespace detail
+
+template <typename T>
+T _maybe_wrap_dim(T dim, T dim_post_expr, bool wrap_scalar = true) {
+  // Inline the fast paths
+  if (C10_LIKELY(dim_post_expr * -1 <= dim && dim < dim_post_expr)) {
+    // For SymInts, we want an explicit control flow to trigger a guard, so we
+    // may as well branch too.
+    if (dim < 0) {
+      return dim + dim_post_expr;
+    }
+    return dim;
+  }
+  // Check edge-cases out-of-line (wrapping scalars and out-of-bounds errors)
+  return c10::detail::maybe_wrap_dim_slow<T>(
+      std::move(dim), std::move(dim_post_expr), wrap_scalar);
+}
+
+inline int64_t maybe_wrap_dim(
+    int64_t dim,
+    int64_t dim_post_expr,
+    bool wrap_scalar = true) {
+  return _maybe_wrap_dim(dim, dim_post_expr, wrap_scalar);
+}
+
+inline c10::SymInt maybe_wrap_dim(
+    c10::SymInt dim,
+    c10::SymInt dim_post_expr,
+    bool wrap_scalar = true) {
+  return _maybe_wrap_dim(std::move(dim), std::move(dim_post_expr), wrap_scalar);
+}
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/alignment.h

@@ -0,0 +1,21 @@
+#pragma once
+
+#include <cstddef>
+
+namespace c10 {
+
+#ifdef C10_MOBILE
+// Use 16-byte alignment on mobile
+// - ARM NEON AArch32 and AArch64
+// - x86[-64] < AVX
+constexpr size_t gAlignment = 16;
+#else
+// Use 64-byte alignment should be enough for computation up to AVX512.
+constexpr size_t gAlignment = 64;
+#endif
+
+constexpr size_t gPagesize = 4096;
+// since the default thp pagesize is 2MB, enable thp only
+// for buffers of size 2MB or larger to avoid memory bloating
+constexpr size_t gAlloc_threshold_thp = static_cast<size_t>(2) * 1024 * 1024;
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/impl/DeviceGuardImplInterface.h

@@ -0,0 +1,365 @@
+#pragma once
+
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/core/Stream.h>
+#include <c10/util/Exception.h>
+
+// Just for C10_ANONYMOUS_VARIABLE
+#include <c10/util/Registry.h>
+
+#include <atomic>
+
+namespace c10 {
+
+// Forward declaration
+class DataPtr;
+
+/**
+ * Note [Flags defining the behavior of events]
+ *
+ * PYTORCH_DEFAULT and BACKEND_DEFAULT are valid for all backends. The
+ * BACKEND_DEFAULT is what a particular backend would select if no
+ * flags were given. PYTORCH_DEFAULT is the PyTorch's framework default
+ * choice for events on that backend, which may not be the same.
+ *
+ * The mapping of PYTORCH_DEFAULT and BACKEND_DEFAULT is done by each
+ * backend implementation.
+ */
+enum class EventFlag {
+  // Disable timing
+  PYTORCH_DEFAULT,
+  // Enable timing
+  BACKEND_DEFAULT,
+  // FOR TESTING ONLY
+  INVALID
+};
+
+namespace impl {
+
+/**
+ * DeviceGuardImplInterface represents the virtual interface which provides
+ * functionality to provide an RAII class for device and stream switching,
+ * via DeviceGuard.  Every distinct device type, e.g., CUDA and HIP, is
+ * expected to implement and register an implementation of this interface.
+ * All classes which inherit from DeviceGuardImplInterface should be declared
+ * 'final'.
+ *
+ * This class exists because we provide a unified interface for performing
+ * device guards via DeviceGuard, but we cannot assume that we have actually
+ * compiled against the, e.g., CUDA library, which actually implements
+ * this guard functionality.  In this case, a dynamic dispatch is required
+ * to cross the library boundary.
+ *
+ * If possible, you should directly use implementations of this interface;
+ * those uses will be devirtualized.
+ */
+struct C10_API DeviceGuardImplInterface {
+  DeviceGuardImplInterface() = default;
+  DeviceGuardImplInterface(const DeviceGuardImplInterface&) = default;
+  DeviceGuardImplInterface& operator=(const DeviceGuardImplInterface&) =
+      default;
+  DeviceGuardImplInterface(DeviceGuardImplInterface&&) noexcept = default;
+  DeviceGuardImplInterface& operator=(DeviceGuardImplInterface&&) noexcept =
+      default;
+
+  /**
+   * Return the type of device managed by this guard implementation.
+   */
+  virtual DeviceType type() const = 0;
+
+  /**
+   * Set the current device to Device, and return the previous Device.
+   */
+  virtual Device exchangeDevice(Device) const = 0;
+  // NB: Implementations of exchangeDevice can be a bit boilerplatey.  You might
+  // consider replacing exchangeDevice with a non-virtual function with a baked
+  // in implementation; however, note that this will triple the number of
+  // virtual calls (when you implement exchangeDevice in a final subclass,
+  // the compiler gets to devirtualize everything; it won't do that if you don't
+  // define it in the subclass!)  A common way to solve this problem is to use
+  // some sort of CRTP; however, we can template DeviceGuardImplInterface since
+  // we really *do* need it to be virtual.  A little boilerplate seems easiest
+  // to explain.  (Another way around this problem is to provide inline
+  // functions that provide the default implementations, but this seems a little
+  // hard to explain.  In any case, we're only going to have on order of ten
+  // implementations of this anyway.)
+
+  /**
+   * Get the current device.
+   */
+  virtual Device getDevice() const = 0;
+
+  /**
+   * Set the current device to Device.
+   */
+  virtual void setDevice(Device) const = 0;
+
+  /**
+   * Set the current device to Device, without checking for errors
+   * (so, e.g., this can be called from a destructor).
+   */
+  virtual void uncheckedSetDevice(Device) const noexcept = 0;
+
+  /**
+   * Get the current stream for a given device.
+   */
+  virtual Stream getStream(Device) const noexcept = 0;
+
+  /**
+   * Get the default stream for a given device.
+   */
+  virtual Stream getDefaultStream(Device) const {
+    TORCH_CHECK(false, "Backend doesn't support acquiring a default stream.")
+  }
+
+  /**
+   * Get a stream from the global pool for a given device.
+   */
+  virtual Stream getStreamFromGlobalPool(Device, bool isHighPriority = false)
+      const {
+    (void)isHighPriority; // Suppress unused variable warning
+    TORCH_CHECK(false, "Backend doesn't support acquiring a stream from pool.")
+  }
+
+  /**
+   * Return a new stream for a given device and priority. The stream will be
+   * copied and shared around, device backend should be able to correctly handle
+   * the lifetime of the stream.
+   */
+  virtual Stream getNewStream(Device, int priority = 0) const {
+    (void)priority;
+    TORCH_CHECK(false, "Backend doesn't support create a new Stream.")
+  }
+
+  /**
+   * Set a stream to be the thread local current stream for its device.
+   * Return the previous stream for that device. You are NOT required
+   * to set the current device to match the device of this stream.
+   */
+  virtual Stream exchangeStream(Stream) const noexcept = 0;
+
+  /**
+   * Destroys the given event.
+   */
+  virtual void destroyEvent(void* /*event*/, const DeviceIndex /*device_index*/)
+      const noexcept {}
+
+  /**
+   * Increments the event's version and enqueues a job with this version
+   * in the stream's work queue. When the stream process that job
+   * it notifies all streams waiting on / blocked by that version of the
+   * event to continue and marks that version as recorded.
+   * */
+  virtual void record(
+      void** /*event*/,
+      const Stream& /*stream*/,
+      const DeviceIndex /*device_index*/,
+      const c10::EventFlag /*flag*/) const {
+    TORCH_CHECK(false, "Backend doesn't support events.");
+  }
+
+  /**
+   * Does nothing if the event has not been scheduled to be recorded.
+   * If the event was previously enqueued to be recorded, a command
+   * to wait for the version of the event that exists at the time of this call
+   * is inserted in the stream's work queue.
+   * When the stream reaches this command it will stop processing
+   * additional commands until that version of the event is marked as recorded.
+   */
+  virtual void block(void* /*event*/, const Stream& /*stream*/) const {
+    TORCH_CHECK(false, "Backend doesn't support events.");
+  }
+
+  /**
+   * Returns true if (and only if)
+   *  (1) the event has never been scheduled to be recorded
+   *  (2) the current version is marked as recorded.
+   * Returns false otherwise.
+   */
+  virtual bool queryEvent(void* /*event*/) const {
+    TORCH_CHECK(false, "Backend doesn't support events.");
+  }
+
+  /**
+   * Get the number of devices.  WARNING: This is REQUIRED to not raise
+   * an exception.  If there is some sort of problem, e.g., driver error,
+   * you should report that there are zero available devices.
+   */
+  virtual DeviceIndex deviceCount() const noexcept = 0;
+
+  /**
+   * Return true if all the work previously enqueued on the stream for
+   * asynchronous execution has completed running on the device.
+   */
+  virtual bool queryStream(const Stream& /*stream*/) const {
+    TORCH_CHECK(false, "Backend doesn't support querying streams.");
+  }
+
+  /**
+   * Wait (by blocking the calling thread) until all the work previously
+   * enqueued on the stream has completed running on the device.
+   */
+  virtual void synchronizeStream(const Stream& /*stream*/) const {
+    TORCH_CHECK(false, "Backend doesn't support synchronizing streams.");
+  }
+
+  /**
+   * Wait (by blocking the calling thread) until all the work previously
+   * recorded on the event has completed running on the device.
+   */
+  virtual void synchronizeEvent(void* /*event*/) const {
+    TORCH_CHECK(false, "Backend doesn't support synchronizing events.");
+  }
+
+  /**
+   * Ensure the caching allocator (if any) is aware that the given DataPtr is
+   * being used on the given stream, and that it should thus avoid recycling the
+   * DataPtr until all work on that stream is done.
+   */
+  virtual void recordDataPtrOnStream(const c10::DataPtr&, const Stream&) const {
+  }
+
+  /**
+   * Fetch the elapsed time between two recorded events.
+   */
+  virtual double elapsedTime(
+      void* /*event1*/,
+      void* /*event2*/,
+      const DeviceIndex /*device_index*/) const {
+    TORCH_CHECK(false, "Backend doesn't support elapsedTime.");
+  }
+
+  /**
+   * Intended use of this class is to leak the DeviceGuardImpl at program end.
+   * So you better not call the destructor, buster!
+   */
+  virtual ~DeviceGuardImplInterface() = default;
+};
+
+// A no-op device guard impl that doesn't do anything interesting.  Useful
+// for devices that don't actually have a concept of device index.  Prominent
+// examples are CPU and Meta.
+template <DeviceType D>
+struct NoOpDeviceGuardImpl final : public DeviceGuardImplInterface {
+  NoOpDeviceGuardImpl() = default;
+  DeviceType type() const override {
+    return D;
+  }
+  Device exchangeDevice(Device) const override {
+    return Device(D, -1); // no-op
+  }
+  Device getDevice() const override {
+    return Device(D, -1);
+  }
+  void setDevice(Device) const override {
+    // no-op
+  }
+  void uncheckedSetDevice(Device) const noexcept override {
+    // no-op
+  }
+  Stream getStream(Device) const noexcept override {
+    // no-op
+    return Stream(Stream::DEFAULT, Device(D, -1));
+  }
+
+  Stream getNewStream(Device, int priority = 0) const override {
+    // no-op
+    (void)priority;
+    return Stream(Stream::DEFAULT, Device(D, -1));
+  }
+
+  // NB: These do NOT set the current device
+  Stream exchangeStream(Stream) const noexcept override {
+    // no-op
+    return Stream(Stream::DEFAULT, Device(D, -1));
+  }
+  DeviceIndex deviceCount() const noexcept override {
+    return 1;
+  }
+
+  // Event-related functions
+  void record(
+      void** /*event*/,
+      const Stream& /*stream*/,
+      const DeviceIndex /*device_index*/,
+      const EventFlag /*flag*/) const override {
+    TORCH_CHECK(false, D, " backend doesn't support events.");
+  }
+  void block(void* /*event*/, const Stream& /*stream*/) const override {
+    TORCH_CHECK(false, D, " backend doesn't support events.")
+  }
+  bool queryEvent(void* /*event*/) const override {
+    TORCH_CHECK(false, D, " backend doesn't support events.")
+  }
+  void destroyEvent(void* /*event*/, const DeviceIndex /*device_index*/)
+      const noexcept override {}
+
+  // Stream-related functions
+  bool queryStream(const Stream& /*stream*/) const override {
+    return true;
+  }
+  void synchronizeStream(const Stream& /*stream*/) const override {
+    // Don't wait for anything.
+  }
+};
+
+// The registry is NON-owning.  Each stored pointer is std::atomic so
+// that under all interleavings of registry calls the structure is
+// race-free.  This doesn't cost us anything on reads in X86.  (An
+// unsynchronized implementation probably is OK too, but I didn't want
+// to prove that we never read from device_guard_impl_registry at the
+// same time some registration is occurring.  Shiver.)
+//
+// I'd like this registry to be valid even at program destruction time
+// (in case someone uses a DeviceGuard in a destructor to do some cleanup
+// in the CUDA API.)  Since there are no direct accesses of the underlying
+// owning objects which I can use to enforce initialization order (unlike
+// in a Meyer singleton), it implies that you must *leak* objects when
+// putting them in the registry.  This is done by deleting the destructor
+// on DeviceGuardImplInterface.
+// NOLINTNEXTLINE(*c-arrays*)
+extern C10_API std::atomic<const DeviceGuardImplInterface*>
+    device_guard_impl_registry[static_cast<size_t>(
+        DeviceType::COMPILE_TIME_MAX_DEVICE_TYPES)];
+
+// I can't conveniently use c10/util/Registry.h for the following reason:
+// c10/util/Registry.h gives me a slow way of Create'ing a object of some
+// interface from the registry, but no way of quickly accessing an already
+// created object.  I'll be banging on getDeviceGuardImpl every time we do a
+// DeviceGuard, so I really don't want to be doing an unordered_map lookup.
+// Better if the registration mechanism directly drops its implementation
+// into device_guard_impl_registry.
+
+class C10_API DeviceGuardImplRegistrar {
+ public:
+  DeviceGuardImplRegistrar(DeviceType, const DeviceGuardImplInterface*);
+};
+
+#define C10_REGISTER_GUARD_IMPL(DevType, DeviceGuardImpl)              \
+  static ::c10::impl::DeviceGuardImplRegistrar C10_ANONYMOUS_VARIABLE( \
+      g_##DeviceType)(::c10::DeviceType::DevType, new DeviceGuardImpl());
+
+inline const DeviceGuardImplInterface* getDeviceGuardImpl(DeviceType type) {
+  // Two adjacent int16_t fields DeviceType and DeviceIndex has field access
+  // miscompiled on NVCC. To workaround this issue, we apply a mask to the
+  // DeviceType. First check if the DeviceType is 16-bit.
+  // FB employees can see
+  //   https://fb.workplace.com/groups/llvm.gcc/permalink/4053565044692080/
+  // for more details
+  static_assert(sizeof(DeviceType) == 1, "DeviceType is not 8-bit");
+  auto p = device_guard_impl_registry[static_cast<size_t>(type) & 0xFF].load();
+
+  // This seems to be the first place where you make use of a device
+  // when you pass devices to factory functions.  Give a nicer error
+  // message in this case.
+  TORCH_CHECK(p, "PyTorch is not linked with support for ", type, " devices");
+  return p;
+}
+
+inline bool hasDeviceGuardImpl(DeviceType type) {
+  return device_guard_impl_registry[static_cast<size_t>(type)].load();
+}
+
+} // namespace impl
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/core/impl/HermeticPyObjectTLS.h

@@ -0,0 +1,59 @@
+#pragma once
+
+#include <c10/macros/Export.h>
+#include <atomic>
+
+namespace c10::impl {
+
+// This TLS controls whether or not we permanently associate PyObject
+// with Tensor the first time it is allocated.  When hermetic PyObject
+// TLS is enabled (state is true), we DO NOT save PyObjects to Tensor,
+// meaning you get a distinct PyObject whenever you execute the code in
+// question.
+struct C10_API HermeticPyObjectTLS {
+  static void set_state(bool state);
+  static bool get_state() {
+    // Hypothetical fastpath if torchdeploy/multipy isn't used.  Per
+    // https://www.open-std.org/jtc1/sc22/wg21/docs/papers/2020/p2055r0.pdf
+    // this qualifies relaxed access because it is a single-location data
+    // structure (only the boolean here).
+    //
+    // Forgetting about data races for a moment, is there a logical race?
+    //
+    //  - Boolean only ever transitions from false to true.  So the
+    //    critical situation is when one interpreter is already running
+    //    when a second interpreter switches haveState from false to true.
+    //
+    //  - The first interpreter is indifferent whether or not it sees
+    //    hasState true/false; obviously false works (this is what the
+    //    interpreter was previously using; more directly, the interpreter
+    //    calls into itself as the handler, so being hermetic is not
+    //    required), and true simply means serviced python operator calls will
+    //    be hermetic; in these cases it is expected to be functionally
+    //    equivalent.
+    //
+    //  - The second interpreter MUST see hasState true (as its requests will
+    //    be forwarded to the first interpreter), but it is assumed that there
+    //    is a synchronization between the interpreter initialization, and
+    //    when we actually perform operations, so it is guaranteed to see
+    //    hasState true.
+    //
+    // QED.
+    //
+    // This fastpath is currently disabled so that we can more easily test that
+    // hermetic mode works correctly even on stock build of PyTorch.
+    if (false && !haveState_.load(std::memory_order_relaxed))
+      return false;
+    return get_tls_state();
+  }
+  // Call this from the multipy/torchdeploy top level
+  static void init_state();
+
+ private:
+  // This only flipped once from false to true during torchdeploy/multipy
+  // initialization, and never again.
+  static std::atomic<bool> haveState_;
+  static bool get_tls_state();
+};
+
+} // namespace c10::impl

parrot/lib/python3.10/site-packages/torch/include/c10/core/impl/InlineDeviceGuard.h

@@ -0,0 +1,428 @@
+#pragma once
+
+// This file provides implementations of InlineDeviceGuard and
+// InlineOptionalDeviceGuard.
+
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/core/impl/DeviceGuardImplInterface.h>
+#include <c10/core/impl/VirtualGuardImpl.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Optional.h>
+#include <type_traits>
+#include <utility>
+
+namespace c10::impl {
+
+/**
+ * A DeviceGuard is an RAII class that sets a device to some value
+ * on construction, and resets the device to its original value on
+ * destruction.
+ *
+ * InlineDeviceGuard is a helper class for implementing DeviceGuards.
+ * It is templated over a DeviceGuardImpl (anything that implements
+ * DeviceGuardImplInterface).  There are two primary ways to instantiate
+ * InlineDeviceGuard:
+ *
+ *  - With a concrete implementation of DeviceGuardImpl, e.g., CUDAGuardImpl.
+ *    This is the best way to use InlineDeviceGuard, as all calls are
+ *    devirtualized, giving you code as efficient as straight line
+ *    calls to cudaGetDevice/cudaSetDevice.
+ *
+ *  - With VirtualGuardImpl, which does a virtual dispatch to a DeviceGuardImpl
+ *    retrieved from a DeviceType registry.  We have explicitly instantiated
+ *    InlineDeviceGuard this way as c10::DeviceGuard.
+ *
+ * If you are in a hurry, you can use InlineDeviceGuard directly:
+ *
+ *    using CUDAGuard = impl::InlineDeviceGuard<CUDAGuardImpl>;
+ *
+ * However, you can provide a better user experience if you explicitly write a
+ * wrapper class that itself contains the template instantiation:
+ *
+ *    class CUDAGuard {
+ *    public:
+ *      // ... the API ...
+ *    private:
+ *      impl::InlineDeviceGuard<CUDAGuardImpl> guard_;
+ *    }
+ *
+ * The wrapper class provides a good place to write documentation, and helps
+ * avoid weird template instantiation errors when a user incorrectly uses the
+ * class.
+ *
+ * If you need to test this class, consider instantiating it with FakeGuardImpl.
+ */
+template <typename T>
+class InlineDeviceGuard {
+ public:
+  // Note [Omitted default constructor from RAII]
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  // In principle, we could add a default constructor to
+  // DeviceGuard which reads the current device and promises to
+  // restore to that device on exit.  However, most cases where you
+  // would have written this, you probably meant to actually just
+  // use OptionalDeviceGuard (since you don't actually need the
+  // restore to happen if you don't ever actually set the device).
+  // We remove the constructor here to encourage you to think about
+  // what you actually want to happen.
+  explicit InlineDeviceGuard() = delete;
+
+  /// Set the current device to the passed Device.
+  explicit InlineDeviceGuard(Device device)
+      : impl_(device.type()),
+        original_device_(
+            device.index() == -1 ? impl_.getDevice()
+                                 : impl_.exchangeDevice(device)),
+        current_device_(device.index() == -1 ? original_device_ : device) {}
+
+  /// Set the current device index to the passed DeviceIndex.  (The
+  /// device type is inferred from the template parameter T).
+  template <
+      typename U = T,
+      typename =
+          typename std::enable_if_t<!std::is_same_v<U, VirtualGuardImpl>>>
+  explicit InlineDeviceGuard(DeviceIndex device_index)
+      : InlineDeviceGuard(Device(U::static_type, device_index)) {}
+
+  /// Construct an InlineDeviceGuard using VirtualGuardImpl with an explicit
+  /// DeviceGuardImplInterface pointer.
+  template <
+      typename U = T,
+      typename = typename std::enable_if_t<std::is_same_v<U, VirtualGuardImpl>>>
+  explicit InlineDeviceGuard(
+      Device device,
+      const DeviceGuardImplInterface* impl)
+      : impl_(
+            VirtualGuardImpl(impl ? impl : getDeviceGuardImpl(device.type()))),
+        original_device_(
+            device.index() == -1 ? impl_.getDevice()
+                                 : impl_.exchangeDevice(device)),
+        current_device_(device.index() == -1 ? original_device_ : device) {}
+
+  /// Copy is disallowed
+  InlineDeviceGuard(const InlineDeviceGuard<T>&) = delete;
+  InlineDeviceGuard<T>& operator=(const InlineDeviceGuard<T>&) = delete;
+
+  /// Move is disallowed, as DeviceGuard does not have an uninitialized state,
+  /// which is required for moves on types with nontrivial destructors.
+  InlineDeviceGuard(InlineDeviceGuard<T>&& other) = delete;
+  InlineDeviceGuard& operator=(InlineDeviceGuard<T>&& other) = delete;
+
+  ~InlineDeviceGuard() {
+    impl_.uncheckedSetDevice(original_device_);
+  }
+
+  /// Sets the device to the given one.
+  template <
+      typename U = T,
+      typename std::enable_if_t<!std::is_same_v<U, VirtualGuardImpl>, int> = 0>
+  void set_device(at::Device device) {
+    AT_ASSERT(
+        (U::static_type == DeviceType::HIP && device.is_cuda()) ||
+        device.type() == U::static_type);
+    auto index = device.index();
+    if (index == -1)
+      return;
+    impl_.setDevice(device);
+    current_device_ = device;
+  }
+
+  /// Resets the currently set device to its original device, and then sets the
+  /// current device to the passed device.  This is effectively equivalent to
+  /// set_device when a guard supports only a single device type.
+  template <typename U = T>
+  typename std::enable_if_t<!std::is_same_v<U, VirtualGuardImpl>> reset_device(
+      at::Device device) {
+    set_device(device);
+  }
+
+  /// Resets the currently set device to its original device, and then sets the
+  /// current device to the passed device (for a possibly different device
+  /// type).
+  ///
+  /// This method is named reset_device to highlight the fact that previous
+  /// device settings from this guard are NOT preserved, even if the device
+  /// has a different device type.  For example:
+  ///
+  ///   // CUDA device is 0
+  ///   DeviceGuard g(Device(kCUDA, 1));
+  ///   g.reset_device(Device(kHIP, 2));
+  ///   // CUDA device is 0 (!!)
+  ///
+  /// NOTE: this implementation may skip some device setting if it can prove
+  /// that it is unnecessary.
+  ///
+  /// Optional argument is for testing only.
+  template <typename U = T>
+  typename std::enable_if_t<std::is_same_v<U, VirtualGuardImpl>> reset_device(
+      at::Device device,
+      const impl::DeviceGuardImplInterface* impl = nullptr) {
+    auto index = device.index();
+    if (index == -1)
+      return;
+    if (device.type() == original_device_.type()) {
+      AT_ASSERT(impl == nullptr || impl->type() == device.type());
+      impl_.setDevice(device);
+      current_device_ = device;
+    } else {
+      // Destruct and reconstruct the DeviceGuard in place
+      impl_.setDevice(original_device_);
+      impl_ = !impl ? VirtualGuardImpl(device.type()) : VirtualGuardImpl(impl);
+      original_device_ = impl_.exchangeDevice(device);
+      current_device_ = device;
+    }
+  }
+
+  /// Sets the device index to the given one.  The device type is inferred
+  /// from the original device type.
+  void set_index(DeviceIndex index) {
+    reset_device(Device(original_device_.type(), index));
+  }
+
+  /// Returns the device that was set at the time the most recent
+  /// reset_device(), or otherwise the device at construction time.
+  Device original_device() const {
+    return original_device_;
+  }
+
+  /// Returns the most recent device that was set using this device guard,
+  /// either from construction, or via set_device/reset_device/set_index.
+  Device current_device() const {
+    return current_device_;
+  }
+
+ protected:
+  T impl_;
+
+ private:
+  Device original_device_;
+  Device current_device_;
+};
+
+/**
+ * A OptionalDeviceGuard is an RAII class that sets a device to some value on
+ * initialization, and resets the device to its original value on destruction.
+ *
+ * InlineOptionalDeviceGuard is a helper class for implementing
+ * OptionalDeviceGuards.  See guidance in InlineDeviceGuard on how to
+ * use this.  See OptionalDeviceGuard for user-oriented usage notes.
+ */
+template <typename T>
+class InlineOptionalDeviceGuard {
+ public:
+  // Note [Explicit initialization of optional fields]
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  // Explicit initialization of optional fields
+  // required to workaround an nvcc bug; see
+  // https://github.com/pytorch/pytorch/issues/12117
+
+  /// Creates an uninitialized OptionalDeviceGuard.
+  explicit InlineOptionalDeviceGuard()
+      : guard_() // See Note [Explicit initialization of optional fields]
+  {}
+
+  /// Set the current device to the passed Device, if it is not nullopt.
+  explicit InlineOptionalDeviceGuard(optional<Device> device_opt)
+      : guard_() { // See Note [Explicit initialization of optional fields]
+    if (device_opt.has_value()) {
+      guard_.emplace(device_opt.value());
+    }
+  }
+
+  /// Set the current device to the passed DeviceIndex, if it is not nullopt.
+  template <
+      typename U = T,
+      typename =
+          typename std::enable_if_t<!std::is_same_v<U, VirtualGuardImpl>>>
+  explicit InlineOptionalDeviceGuard(optional<DeviceIndex> device_index_opt)
+      : guard_() { // See Note [Explicit initialization of optional fields]
+    if (device_index_opt.has_value()) {
+      guard_.emplace(device_index_opt.value());
+    }
+  }
+
+  /// All constructors of DeviceGuard are valid for OptionalDeviceGuard
+  /// and result in initialized OptionalDeviceGuard.
+  template <typename... Args>
+  explicit InlineOptionalDeviceGuard(Args&&... args)
+      : guard_(std::in_place, std::forward<Args>(args)...) {}
+
+  // TODO: Consider reading Tensor and TensorList constructors here, when
+  // Tensor moves to c10.  (These are only valid on OptionalDeviceGuard,
+  // because a Tensor may be undefined, in which case we need an uninitialized
+  // tensor guard.)
+
+  // Note [Move construction for RAII guards is tricky]
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  // In principle, move construction is useful for terminating
+  // the lifetime of a `OptionalDeviceGuard` early; for example:
+  //
+  //     // current device is d0
+  //     OptionalDeviceGuard g1(d1);
+  //     // current device is d1
+  //     {
+  //       OptionalDeviceGuard g2(std::move(g1));
+  //     }
+  //     // current device is d0!!
+  //
+  // However, it's difficult to implement the move constructor
+  // in a way that works in all situations.  For example, consider
+  // the following example:
+  //
+  //     OptionalDeviceGuard g1(d1);
+  //     {
+  //       OptionalDeviceGuard g2(d2);
+  //       {
+  //         OptionalDeviceGuard g3(std::move(g1)); // !!!
+  //       }
+  //     }
+  //
+  // What should the current device be while g3 in scope... and what
+  // should it be after it goes out of scope?  What about g2?
+  // There don't seem to be satisfactory answers for these questions.
+  //
+  // It's in principle possible to raise an error when this occurs
+  // by doing some extra thread-local bookkeeping.  But why bother?
+  // Just don't provide the constructor.
+  InlineOptionalDeviceGuard(InlineOptionalDeviceGuard<T>&& other) = delete;
+
+  // Note [Move assignment for RAII guards is tricky]
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  // Move assignment is deleted, because you need to know which guard was
+  // defined "first", as that guard's original_device_ wins--with the current
+  // representation, we have no way of telling which is the case.  (Move
+  // construction does not have this problem, as one guard is always
+  // uninitialized.)
+  //
+  // We can make this clear by way of a pair of examples:
+  //
+  // Example 1:
+  //
+  //  // initial device is n0
+  //  {
+  //    CUDAGuard g1(n1);
+  //    {
+  //      CUDAGuard g2(n2);
+  //      // current device should be n2
+  //      g1 = std::move(g2);
+  //      // current device should still be n2
+  //    }
+  //    // current device should still be n2
+  //  }
+  //  // current device should be n0
+  //
+  //  Example 2 (flip the order of the two guards):
+  //
+  //  // initial device is n0
+  //  {
+  //    CUDAGuard g2(n2);
+  //    {
+  //      CUDAGuard g1(n1);
+  //      // current device should be n1
+  //      g1 = std::move(g2);
+  //      // current device should be n2
+  //    }
+  //    // current device should be n0 (since g2 has been vacated)
+  //  }
+  //
+  // In both examples, we need g1 to restore to n0 after move assignment.
+  // However, in example 1, this is determined by the restore value of g1
+  // (prior to the move). In example 2, however, it is determined by the the
+  // restore value of g2(!!). We don't know which one should win, without having
+  // a way of telling which guard was allocated first.
+  //
+  // We could solve this with an extra thread-local variable.  But no one is
+  // actually using move-assignment.  So just get rid of it.
+  InlineOptionalDeviceGuard& operator=(InlineOptionalDeviceGuard&& other) =
+      delete;
+
+  /// Sets the device to the given one.  Initializes OptionalDeviceGuard if it
+  /// is not already initialized.
+  template <
+      typename U = T,
+      typename =
+          typename std::enable_if_t<!std::is_same_v<U, VirtualGuardImpl>>>
+  void set_device(at::Device device) {
+    if (!guard_.has_value()) {
+      guard_.emplace(device);
+    } else {
+      guard_->set_device(device);
+    }
+  }
+
+  /// Resets the currently set device to its original device, and then sets the
+  /// current device to the passed device (for a possibly different device
+  /// type).  Initializes OptionalDeviceGuard if it is not already initialized.
+  ///
+  /// See notes on why this is called reset_device on InlineDeviceGuard.
+  ///
+  /// Optional argument is for testing only.
+  template <
+      typename U = T,
+      typename = typename std::enable_if_t<std::is_same_v<U, VirtualGuardImpl>>>
+  void reset_device(
+      at::Device device,
+      const DeviceGuardImplInterface* impl = nullptr) {
+    if (!guard_.has_value()) {
+      guard_.emplace(device, impl);
+    } else {
+      guard_->reset_device(device, impl);
+    }
+  }
+
+  /// Resets the currently set device to its original device, and then sets the
+  /// current device to the passed device.  Initializes the guard if it is
+  /// not already initialized.  This is effectively equivalent to set_device
+  /// when a guard supports only a single device type.
+  template <
+      typename U = T,
+      typename =
+          typename std::enable_if_t<!std::is_same_v<U, VirtualGuardImpl>>>
+  void reset_device(at::Device device) {
+    if (!guard_.has_value()) {
+      guard_.emplace(device);
+    } else {
+      guard_->reset_device(device);
+    }
+  }
+
+  /// Sets the device index to the given one.  The device type is statically
+  /// known.
+  template <
+      typename U = T,
+      typename =
+          typename std::enable_if_t<!std::is_same_v<U, VirtualGuardImpl>>>
+  void set_index(DeviceIndex index) {
+    if (!guard_.has_value()) {
+      guard_.emplace(index);
+    } else {
+      guard_->set_index(index);
+    }
+  }
+
+  /// Returns the device that was set immediately prior to initialization of
+  /// the, guard, or nullopt if the guard is uninitialized.
+  optional<Device> original_device() const {
+    return guard_.has_value() ? make_optional(guard_->original_device())
+                              : nullopt;
+  }
+
+  /// Returns the most recent device that was set using this device guard,
+  /// either from construction, or via set_device, if the guard is initialized,
+  /// or nullopt if the guard is uninitialized.
+  optional<Device> current_device() const {
+    return guard_.has_value() ? make_optional(guard_->current_device())
+                              : nullopt;
+  }
+
+  /// Restore the original device, resetting this guard to uninitialized state.
+  void reset() {
+    guard_.reset();
+  }
+
+ private:
+  optional<InlineDeviceGuard<T>> guard_;
+};
+
+} // namespace c10::impl

parrot/lib/python3.10/site-packages/torch/include/c10/core/impl/InlineStreamGuard.h

@@ -0,0 +1,255 @@
+#pragma once
+
+#include <c10/core/impl/InlineDeviceGuard.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/irange.h>
+
+namespace c10::impl {
+
+/**
+ * A StreamGuard is an RAII class that changes the current device
+ * to the device corresponding to some stream, and changes the
+ * default stream on that device to be this stream.
+ *
+ * InlineStreamGuard is a helper class for implementing StreamGuards.
+ * See InlineDeviceGuard for guidance on how to use this class.
+ */
+template <typename T>
+class InlineStreamGuard : private InlineDeviceGuard<T> {
+ public:
+  /// No default constructor, see Note [Omitted default constructor from RAII]
+  explicit InlineStreamGuard() = delete;
+
+  /// Set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream.
+  explicit InlineStreamGuard(Stream stream)
+      : InlineDeviceGuard<T>(stream.device()),
+        original_stream_of_original_device_(
+            this->impl_.getStream(original_device())),
+        original_stream_of_current_device_(this->impl_.exchangeStream(stream)),
+        current_stream_(stream) {}
+
+  /// This constructor exists purely for testing
+  template <
+      typename U = T,
+      typename = typename std::enable_if_t<std::is_same_v<U, VirtualGuardImpl>>>
+  explicit InlineStreamGuard(
+      Stream stream,
+      const DeviceGuardImplInterface* impl)
+      : InlineDeviceGuard<T>(
+            stream.device(),
+            impl ? impl : getDeviceGuardImpl(stream.device_type())),
+        original_stream_of_original_device_(
+            this->impl_.getStream(original_device())),
+        original_stream_of_current_device_(this->impl_.exchangeStream(stream)),
+        current_stream_(stream) {}
+
+  /// Copy is disallowed
+  InlineStreamGuard(const InlineStreamGuard<T>&) = delete;
+  InlineStreamGuard<T>& operator=(const InlineStreamGuard<T>&) = delete;
+
+  /// Move is disallowed, as StreamGuard does not have an uninitialized state,
+  /// which is required for moves on types with nontrivial destructors.
+  InlineStreamGuard(InlineStreamGuard<T>&& other) = delete;
+  InlineStreamGuard& operator=(InlineStreamGuard<T>&& other) = delete;
+
+  ~InlineStreamGuard() {
+    this->impl_.exchangeStream(original_stream_of_current_device_);
+  }
+
+  /// Resets the currently set stream to the original stream and
+  /// the currently set device to the original device.  Then,
+  /// set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream.
+  ///
+  /// NOTE: this implementation may skip some stream/device setting if
+  /// it can prove that it is unnecessary.
+  ///
+  /// WARNING: reset_stream does NOT preserve previously set streams on
+  /// different devices.  If you need to set streams on multiple devices
+  /// use MultiStreamGuard instead.
+  void reset_stream(Stream stream) {
+    // TODO: make a version that takes an impl argument.  Unfortunately,
+    // that will require SFINAE because impl is only valid for the
+    // VirtualGuardImpl specialization.
+    if (stream.device() == this->current_device()) {
+      this->impl_.exchangeStream(stream);
+      current_stream_ = stream;
+    } else {
+      // Destruct and reconstruct the StreamGuard in-place
+      this->impl_.exchangeStream(original_stream_of_current_device_);
+      this->reset_device(stream.device());
+      original_stream_of_current_device_ = this->impl_.exchangeStream(stream);
+      current_stream_ = stream;
+    }
+  }
+
+  // It's not clear if set_device should also reset the current stream
+  // if the device is unchanged; therefore, we don't provide it.
+  // The situation is somewhat clearer with reset_device, but it's still
+  // a pretty weird thing to do, so haven't added this either.
+
+  /// Returns the stream of the original device prior to this guard.  Subtly,
+  /// the stream returned here is the original stream of the *original*
+  /// device; i.e., it's the stream that your computation *would* have
+  /// been put on, if it hadn't been for this meddling stream guard.
+  /// This is usually what you want.
+  Stream original_stream() const {
+    return original_stream_of_original_device_;
+  }
+
+  /// Returns the most recent stream that was set using this device guard,
+  /// either from construction, or via set_stream.
+  Stream current_stream() const {
+    return current_stream_;
+  }
+
+  /// Returns the most recent device that was set using this device guard,
+  /// either from construction, or via set_device/reset_device/set_index.
+  Device current_device() const {
+    return InlineDeviceGuard<T>::current_device();
+  }
+
+  /// Returns the device that was set at the most recent reset_stream(),
+  /// or otherwise the device at construction time.
+  Device original_device() const {
+    return InlineDeviceGuard<T>::original_device();
+  }
+
+ private:
+  Stream
+      original_stream_of_original_device_; // what the user probably cares about
+  Stream original_stream_of_current_device_; // what we need to restore
+  Stream current_stream_;
+};
+
+/**
+ * An OptionalStreamGuard is an RAII class that sets a device to some value on
+ * initialization, and resets the device to its original value on destruction.
+ * See InlineOptionalDeviceGuard for more guidance on how to use this class.
+ */
+template <typename T>
+class InlineOptionalStreamGuard {
+ public:
+  /// Creates an uninitialized stream guard.
+  explicit InlineOptionalStreamGuard()
+      : guard_() // See Note [Explicit initialization of optional fields]
+  {}
+
+  /// Set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream,
+  /// if the passed stream is not nullopt.
+  explicit InlineOptionalStreamGuard(optional<Stream> stream_opt) : guard_() {
+    if (stream_opt.has_value()) {
+      guard_.emplace(stream_opt.value());
+    }
+  }
+
+  /// All constructors of StreamGuard are valid for OptionalStreamGuard
+  template <typename... Args>
+  explicit InlineOptionalStreamGuard(Args&&... args)
+      : guard_(std::in_place, std::forward<Args>(args)...) {}
+
+  // See Note [Move construction for RAII guards is tricky]
+  InlineOptionalStreamGuard(InlineOptionalStreamGuard<T>&& other) = delete;
+
+  // See Note [Move assignment for RAII guards is tricky]
+  InlineOptionalStreamGuard& operator=(InlineOptionalStreamGuard&& other) =
+      delete;
+
+  /// Resets the currently set stream to the original stream and
+  /// the currently set device to the original device.  Then,
+  /// set the current device to the device associated with the passed stream,
+  /// and set the current stream on that device to the passed stream.
+  /// Initializes the OptionalStreamGuard if it was not previously initialized.
+  void reset_stream(Stream stream) {
+    if (guard_.has_value()) {
+      guard_->reset_stream(stream);
+    } else {
+      guard_.emplace(stream);
+    }
+  }
+
+  /// Returns the stream that was set at the time the guard was most recently
+  /// initialized, or nullopt if the guard is uninitialized.
+  optional<Stream> original_stream() const {
+    return guard_.has_value() ? make_optional(guard_->original_stream())
+                              : nullopt;
+  }
+
+  /// Returns the most recent stream that was set using this stream guard,
+  /// either from construction, or via reset_stream, if the guard is
+  /// initialized, or nullopt if the guard is uninitialized.
+  optional<Stream> current_stream() const {
+    return guard_.has_value() ? make_optional(guard_->current_stream())
+                              : nullopt;
+  }
+
+  /// Restore the original device and stream, resetting this guard to
+  /// uninitialized state.
+  void reset() {
+    guard_.reset();
+  }
+
+ private:
+  optional<InlineStreamGuard<T>> guard_;
+};
+
+template <typename T>
+class InlineMultiStreamGuard {
+ public:
+  /// Calls `set_stream` on each of the streams in the list.
+  /// This may be useful if you need to set different streams
+  /// for different devices.
+  explicit InlineMultiStreamGuard(ArrayRef<Stream> streams) {
+    if (!streams.empty()) {
+      impl_.emplace(getDeviceTypeOfStreams(streams));
+      original_streams_.reserve(streams.size());
+      for (const Stream& s : streams) {
+        original_streams_.emplace_back(this->impl_->exchangeStream(s));
+      }
+    }
+  }
+
+  /// Copy is disallowed
+  InlineMultiStreamGuard(const InlineMultiStreamGuard&) = delete;
+  InlineMultiStreamGuard<T>& operator=(const InlineMultiStreamGuard&) = delete;
+
+  /// Move is disallowed, as StreamGuard does not have an uninitialized state,
+  /// which is required for moves on types with nontrivial destructors.
+  InlineMultiStreamGuard(InlineMultiStreamGuard&& other) = delete;
+  InlineMultiStreamGuard& operator=(InlineMultiStreamGuard&& other) = delete;
+
+  ~InlineMultiStreamGuard() noexcept {
+    if (this->impl_.has_value()) {
+      for (const Stream& s : original_streams_) {
+        this->impl_->exchangeStream(s);
+      }
+    }
+  }
+
+ protected:
+  optional<T> impl_;
+
+ private:
+  /// The original streams that were active on all devices.
+  std::vector<Stream> original_streams_;
+
+  static DeviceType getDeviceTypeOfStreams(ArrayRef<Stream> streams) {
+    TORCH_INTERNAL_ASSERT(!streams.empty());
+    DeviceType type = streams[0].device_type();
+    for (const auto idx : c10::irange(1, streams.size())) {
+      TORCH_CHECK_VALUE(
+          streams[idx].device_type() == type,
+          "Streams have a mix of device types: stream 0 is on ",
+          streams[0].device(),
+          " while stream ",
+          idx,
+          " is on device ",
+          streams[idx].device());
+    }
+    return type;
+  }
+};
+
+} // namespace c10::impl

parrot/lib/python3.10/site-packages/torch/include/c10/core/impl/LocalDispatchKeySet.h

@@ -0,0 +1,164 @@
+#pragma once
+
+#include <c10/core/DispatchKeySet.h>
+#include <c10/macros/Export.h>
+
+// TLS management for DispatchKeySet (the "local" DispatchKeySet(s))
+//
+// This manages two thread-local DispatchKeySets:
+//
+//  - The included type set, which adds a tensor type for consideration
+//    in dispatch.  (For example, you might add Profiling to
+//    the included type set to turn on profiling on all tensor operations.)
+//
+//  - The excluded type set, which disqualifies a tensor type from dispatch.
+//    (For example, after redispatching on variable, we disqualify
+//    Autograd so we don't attempt to handle variable again.)
+//    (Exclusion wins over inclusion.)
+//
+// NB: Originally, I implemented the excluded type set as storing the inverted
+// set, but TLS is defined to be zero-initialized, so this doesn't actually work
+// (if it's inverted, you want the set to be -1 initialized).
+
+namespace c10::impl {
+
+// POD version of LocalDispatchKeySet.  Declared here just so that
+// we can put it in the guards.
+// This struct encapsulates special handling for TLS initialization
+// in set_included()/included() API so that they reflect the truth.
+// If you want to create PODLocalDispatchKeySet with non-zero state,
+// use set_included() instead of default constructor.
+struct C10_API PODLocalDispatchKeySet {
+  uint64_t included_;
+  uint64_t excluded_;
+
+  // See Note [TLS Initialization]
+  DispatchKeySet included() const {
+    return DispatchKeySet(DispatchKeySet::RAW, included_) ^
+        c10::default_included_set;
+  }
+  DispatchKeySet excluded() const {
+    return DispatchKeySet(DispatchKeySet::RAW, excluded_) ^
+        c10::default_excluded_set;
+  }
+
+  void set_included(DispatchKeySet x) {
+    included_ = (x ^ c10::default_included_set).raw_repr();
+  }
+  void set_excluded(DispatchKeySet x) {
+    excluded_ = (x ^ c10::default_excluded_set).raw_repr();
+  }
+};
+static_assert(
+    std::is_trivial_v<PODLocalDispatchKeySet>,
+    "PODLocalDispatchKeySet must be a POD type.");
+
+struct C10_API LocalDispatchKeySet {
+  /* implicit */ LocalDispatchKeySet(PODLocalDispatchKeySet x)
+      : included_(x.included()), excluded_(x.excluded()) {}
+  DispatchKeySet included_;
+  DispatchKeySet excluded_;
+};
+
+// thread_local variables cannot be C10_API on Windows.
+// Inlining this seems to break AutoDispatchBelowAutograd on Android.
+#if defined(_MSC_VER) || defined(C10_ANDROID) || defined(C10_IPHONE)
+C10_API LocalDispatchKeySet tls_local_dispatch_key_set();
+#else // defined(_MSC_VER) || defined(C10_ANDROID) || defined(C10_IPHONE)
+extern C10_API thread_local PODLocalDispatchKeySet raw_local_dispatch_key_set;
+
+inline C10_API LocalDispatchKeySet tls_local_dispatch_key_set() {
+  // Don't let people fiddle with the thread_local directly just
+  // because they include this header.
+  return raw_local_dispatch_key_set;
+}
+#endif // defined(_MSC_VER) || defined(C10_ANDROID) || defined(C10_IPHONE)
+
+// Internal, use ThreadLocalStateGuard
+C10_API void _force_tls_local_dispatch_key_set(LocalDispatchKeySet key_set);
+
+// RAII API for manipulating the thread-local dispatch state.
+
+class C10_API IncludeDispatchKeyGuard {
+ public:
+  IncludeDispatchKeyGuard(DispatchKeySet);
+  IncludeDispatchKeyGuard(DispatchKey k)
+      : IncludeDispatchKeyGuard(DispatchKeySet(k)) {}
+  IncludeDispatchKeyGuard(const IncludeDispatchKeyGuard&) = delete;
+  IncludeDispatchKeyGuard operator=(const IncludeDispatchKeyGuard&) = delete;
+  IncludeDispatchKeyGuard(IncludeDispatchKeyGuard&&) = delete;
+  IncludeDispatchKeyGuard operator=(IncludeDispatchKeyGuard&&) = delete;
+  ~IncludeDispatchKeyGuard();
+
+ private:
+  // A little micro-optimization to save us from tls_get_addr call
+  // on destruction
+  PODLocalDispatchKeySet* tls_;
+  DispatchKeySet include_;
+};
+
+class C10_API ExcludeDispatchKeyGuard {
+ public:
+  ExcludeDispatchKeyGuard(DispatchKeySet);
+  ExcludeDispatchKeyGuard(DispatchKey k)
+      : ExcludeDispatchKeyGuard(DispatchKeySet(k)) {}
+  ExcludeDispatchKeyGuard(const ExcludeDispatchKeyGuard&) = delete;
+  ExcludeDispatchKeyGuard operator=(const ExcludeDispatchKeyGuard&) = delete;
+  ExcludeDispatchKeyGuard(ExcludeDispatchKeyGuard&&) = delete;
+  ExcludeDispatchKeyGuard operator=(ExcludeDispatchKeyGuard&&) = delete;
+  ~ExcludeDispatchKeyGuard();
+
+ private:
+  // A little micro-optimization to save us from tls_get_addr call
+  // on destruction
+  PODLocalDispatchKeySet* tls_;
+  DispatchKeySet exclude_;
+};
+
+struct C10_API ForceDispatchKeyGuard {
+ public:
+  ForceDispatchKeyGuard()
+      : saved_keyset_(c10::impl::tls_local_dispatch_key_set()) {}
+  ForceDispatchKeyGuard(c10::impl::LocalDispatchKeySet key_set)
+      : ForceDispatchKeyGuard() {
+    c10::impl::_force_tls_local_dispatch_key_set(key_set);
+  }
+  ForceDispatchKeyGuard(
+      c10::DispatchKeySet include,
+      c10::DispatchKeySet exclude)
+      : ForceDispatchKeyGuard() {
+    auto updated_set = saved_keyset_;
+    updated_set.included_ = include;
+    updated_set.excluded_ = exclude;
+    c10::impl::_force_tls_local_dispatch_key_set(updated_set);
+  }
+  ~ForceDispatchKeyGuard() {
+    c10::impl::_force_tls_local_dispatch_key_set(saved_keyset_);
+  }
+
+ private:
+  c10::impl::LocalDispatchKeySet saved_keyset_;
+};
+
+// Non-RAII API for manipulating the thread-local dispatch state.
+// Please prefer the RAII API.  The non-RAII API may be useful when
+// the included/excluded state of a given DispatchKey must span
+// many calls from the Python to the C++, so you cannot conveniently
+// use an RAII guard.
+//
+// Example use case:  a Python context manager that includes a certain
+// DispatchKey, to ensure ops running under the context manager dispatch
+// through that DispatchKey's registered overrides.
+//
+// The non-RAII API is less efficient than the RAII guards because both the
+// getter and setter will do a tls_getaddr lookup (the RAII struct only needs
+// one!)
+
+C10_API bool tls_is_dispatch_key_excluded(DispatchKey x);
+C10_API void tls_set_dispatch_key_excluded(DispatchKey x, bool desired_state);
+C10_API bool tls_is_dispatch_key_included(DispatchKey x);
+C10_API void tls_set_dispatch_key_included(DispatchKey x, bool desired_state);
+C10_API bool tls_is_dispatch_keyset_excluded(DispatchKeySet ks);
+C10_API bool tls_is_dispatch_keyset_included(DispatchKeySet ks);
+
+} // namespace c10::impl

parrot/lib/python3.10/site-packages/torch/include/c10/core/impl/PyObjectSlot.h

@@ -0,0 +1,190 @@
+#pragma once
+
+#include <c10/core/impl/HermeticPyObjectTLS.h>
+#include <c10/core/impl/PyInterpreter.h>
+#include <c10/util/Optional.h>
+#include <c10/util/python_stub.h>
+
+#include <atomic>
+
+namespace c10::impl {
+
+struct C10_API PyObjectSlot {
+ public:
+  PyObjectSlot();
+
+  ~PyObjectSlot();
+
+  void maybe_destroy_pyobj();
+
+  // Associate the TensorImpl with the specified PyObject, and, if necessary,
+  // also tag the interpreter.
+  //
+  // NB: This lives in a header so that we can inline away the switch on status
+  //
+  // NB: THIS FUNCTION CAN RAISE AN EXCEPTION.  Make sure to clean up after
+  // PyObject if necessary!
+  void init_pyobj(
+      PyInterpreter* self_interpreter,
+      PyObject* pyobj,
+      PyInterpreterStatus status) {
+    impl::PyInterpreter* expected = nullptr;
+    switch (status) {
+      case impl::PyInterpreterStatus::DEFINITELY_UNINITIALIZED:
+        // caller guarantees there is no multithreaded access; if there is
+        // no data race OK to do a relaxed store
+        pyobj_interpreter_.store(self_interpreter, std::memory_order_relaxed);
+        break;
+      case impl::PyInterpreterStatus::TAGGED_BY_US:
+        // no tagging is necessary, the tag is already correct
+        break;
+      case impl::PyInterpreterStatus::MAYBE_UNINITIALIZED:
+        // attempt to claim this TensorImpl with the specified interpreter
+        // tag
+        if (pyobj_interpreter_.compare_exchange_strong(
+                expected, self_interpreter, std::memory_order_acq_rel)) {
+          break;
+        }
+        // test if, actually, it was already tagged by us!  this situation can't
+        // be caused by a race, but it could be caused by a situation
+        // where someone conservatively tagged the tensor as MAYBE_UNINITIALIZED
+        // (because they didn't pre-check the tag) when actually it was
+        // owned by the interpreter
+        if (expected == self_interpreter) {
+          break;
+        }
+        // fallthrough, we lost the race.  We are guaranteed not to lose the
+        // race with ourself, as calls to init_pyobj with the same interpreter
+        // ID must be sequentialized by the GIL
+        [[fallthrough]];
+      case impl::PyInterpreterStatus::TAGGED_BY_OTHER:
+        TORCH_CHECK(
+            false,
+            "cannot allocate PyObject for Tensor on interpreter ",
+            self_interpreter,
+            " that has already been used by another torch deploy interpreter ",
+            pyobj_interpreter_.load());
+    }
+
+    // we are the ONLY thread that can have gotten to this point.  It is not
+    // possible to conflict with another zero interpreter as access is protected
+    // by GIL
+    // NB: owns_pyobj tag is initially false
+    pyobj_ = pyobj;
+  }
+
+  // Query the PyObject interpreter.  This may return null if there is no
+  // interpreter.  This is racy!
+  PyInterpreter* pyobj_interpreter();
+
+  PyObject* _unchecked_untagged_pyobj() const;
+
+  // Test the interpreter tag.  If tagged for the current interpreter, return
+  // a non-nullopt (but possibly null) PyObject.  If (possibly) untagged,
+  // returns a nullopt.  If it is definitely invalid, raises an error.
+  //
+  // If `ignore_hermetic_tls` is false and this function is called from a
+  // hermetic context (ie, `HermeticPyObjectTLS::get_state()` is true), then
+  // nullopt is returned. If `ignore_hermetic_tls` is true, then the hermetic
+  // context is ignored, allowing you to check the interpreter tag of a
+  // nonhermetic PyObject from within a hermetic context. This is necessary
+  // because there are some cases where the deallocator function of a
+  // nonhermetic PyObject is called from within a hermetic context, so it must
+  // be properly treated as a nonhermetic PyObject.
+  //
+  // NB: this lives in header so that we can avoid actually creating the
+  // std::optional
+  std::optional<PyObject*> check_pyobj(
+      PyInterpreter* self_interpreter,
+      bool ignore_hermetic_tls = false) const {
+    // Note [Memory ordering on Python interpreter tag]
+    impl::PyInterpreter* interpreter =
+        pyobj_interpreter_.load(std::memory_order_acquire);
+    if (interpreter == nullptr) {
+      // NB: This never returns DEFINITELY_UNINITIALIZED because there is
+      // always the possibility that another thread races to initialize
+      // after we query here.  The only time when we can conclude a tensor
+      // is definitely uninitialized is when we have just allocated it and
+      // it cannot have escaped to other threads yet
+      return c10::nullopt;
+    } else if (interpreter == self_interpreter) {
+      // NB: pyobj_ could still be null!
+      if (!ignore_hermetic_tls && c10::impl::HermeticPyObjectTLS::get_state()) {
+        return c10::nullopt;
+      } else {
+        return c10::make_optional(_unchecked_untagged_pyobj());
+      }
+    } else {
+      TORCH_CHECK(
+          false,
+          "cannot access PyObject for Tensor on interpreter ",
+          (*self_interpreter)->name(),
+          " that has already been used by another torch deploy interpreter ",
+          (*pyobj_interpreter_.load())->name());
+    }
+  }
+
+  // Clear the PyObject field for an interpreter, in situations where we
+  // statically know the tensor is tagged with our interpreter.
+  void unchecked_clear_pyobj(PyInterpreter* interpreter);
+
+  PyInterpreter& load_pyobj_interpreter() const;
+
+  // Check if the PyObjectSlot's interpreter is the same as the specified
+  // interpreter
+  bool check_interpreter(PyInterpreter* interpreter);
+
+  // Check if the PyObjectSlot is holding a PyObject, owned or non-owned
+  bool has_pyobj_nonhermetic();
+
+  bool owns_pyobj();
+
+  void set_owns_pyobj(bool b);
+
+ private:
+  // This field contains the interpreter tag for this object.  See
+  // Note [Python interpreter tag] for general context
+  //
+  // Note [Memory ordering on Python interpreter tag]
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  // What memory_order do we need when accessing this atomic?  We don't
+  // need a single total modification order (as provided by
+  // memory_order_seq_cst) as pyobj_interpreter_ is monotonic: it can only
+  // transition from -1 to some positive integer and never changes afterwards.
+  // Because there is only one modification, it trivially already has a total
+  // modification order (e.g., we don't need fences or locked instructions on
+  // x86)
+  //
+  // In fact, one could make a reasonable argument that relaxed reads are OK,
+  // due to the presence of external locking (GIL) to ensure that interactions
+  // with other data structures are still correctly synchronized, so that
+  // we fall in the "Single-Location Data Structures" case as described in
+  // http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2020/p2055r0.pdf
+  // However, on x86, it doesn't matter if I use acquire or relaxed on the load
+  // as I get the same assembly in both cases.  So I just use the more
+  // conservative acquire (which will impede compiler optimizations but I don't
+  // care)
+  std::atomic<PyInterpreter*> pyobj_interpreter_;
+
+  // This field contains a reference to a PyObject representing this Tensor.
+  // If pyobj is nullptr, when we transfer Tensor to Python, we allocate a new
+  // PyObject for it and set this field.  This field does not have to be
+  // protected by an atomic as it is only allowed to be accessed when you hold
+  // the GIL, or during destruction of the tensor.
+  //
+  // When a PyObject dies, you are obligated to clear this field
+  // (otherwise, you will try to use-after-free the pyobj); this currently
+  // occurs in THPVariable_clear in torch/csrc/autograd/python_variable.cpp
+  //
+  // NB: Ordinarily, this should not be a strong reference, as if the
+  // PyObject owns the Tensor, this would create a reference cycle.
+  // However, sometimes this ownership flips.  To track who owns
+  // who, this has a single pointer tag indicating whether or not the
+  // C++ object owns the PyObject (the common case, zero, means PyObject
+  // owns the C++ object); see _unchecked_untagged_pyobj for raw access
+  // or check_pyobj for checked access.  See references to PyObject
+  // resurrection in torch/csrc/autograd/python_variable.cpp
+  PyObject* pyobj_;
+};
+
+} // namespace c10::impl

parrot/lib/python3.10/site-packages/torch/include/c10/core/impl/PythonDispatcherTLS.h

@@ -0,0 +1,24 @@
+#pragma once
+
+#include <c10/core/impl/PyInterpreter.h>
+#include <c10/macros/Export.h>
+
+namespace c10::impl {
+
+struct C10_API PythonDispatcherTLS {
+  static void set_state(PyInterpreter* state);
+  static PyInterpreter* get_state();
+  static void reset_state();
+};
+
+struct C10_API DisablePythonDispatcher {
+  DisablePythonDispatcher() : old_(PythonDispatcherTLS::get_state()) {
+    PythonDispatcherTLS::set_state({});
+  }
+  ~DisablePythonDispatcher() {
+    PythonDispatcherTLS::set_state(old_);
+  }
+  PyInterpreter* old_;
+};
+
+} // namespace c10::impl

parrot/lib/python3.10/site-packages/torch/include/c10/core/impl/alloc_cpu.h

@@ -0,0 +1,12 @@
+#pragma once
+
+#include <c10/macros/Export.h>
+
+#include <cstddef>
+
+namespace c10 {
+
+C10_API void* alloc_cpu(size_t nbytes);
+C10_API void free_cpu(void* data);
+
+} // namespace c10

parrot/lib/python3.10/site-packages/torch/include/c10/macros/Export.h

@@ -0,0 +1,160 @@
+#ifndef C10_MACROS_EXPORT_H_
+#define C10_MACROS_EXPORT_H_
+
+/* Header file to define the common scaffolding for exported symbols.
+ *
+ * Export is by itself a quite tricky situation to deal with, and if you are
+ * hitting this file, make sure you start with the background here:
+ * - Linux: https://gcc.gnu.org/wiki/Visibility
+ * - Windows:
+ * https://docs.microsoft.com/en-us/cpp/cpp/dllexport-dllimport?view=vs-2017
+ *
+ * Do NOT include this file directly. Instead, use c10/macros/Macros.h
+ */
+
+// You do not need to edit this part of file unless you are changing the core
+// pytorch export abstractions.
+//
+// This part defines the C10 core export and import macros. This is controlled
+// by whether we are building shared libraries or not, which is determined
+// during build time and codified in c10/core/cmake_macros.h.
+// When the library is built as a shared lib, EXPORT and IMPORT will contain
+// visibility attributes. If it is being built as a static lib, then EXPORT
+// and IMPORT basically have no effect.
+
+// As a rule of thumb, you should almost NEVER mix static and shared builds for
+// libraries that depend on c10. AKA, if c10 is built as a static library, we
+// recommend everything dependent on c10 to be built statically. If c10 is built
+// as a shared library, everything dependent on it should be built as shared. In
+// the PyTorch project, all native libraries shall use the macro
+// C10_BUILD_SHARED_LIB to check whether pytorch is building shared or static
+// libraries.
+
+// For build systems that do not directly depend on CMake and directly build
+// from the source directory (such as Buck), one may not have a cmake_macros.h
+// file at all. In this case, the build system is responsible for providing
+// correct macro definitions corresponding to the cmake_macros.h.in file.
+//
+// In such scenarios, one should define the macro
+//     C10_USING_CUSTOM_GENERATED_MACROS
+// to inform this header that it does not need to include the cmake_macros.h
+// file.
+
+#ifndef C10_USING_CUSTOM_GENERATED_MACROS
+#include <c10/macros/cmake_macros.h>
+#endif // C10_USING_CUSTOM_GENERATED_MACROS
+
+#ifdef _WIN32
+#define C10_HIDDEN
+#if defined(C10_BUILD_SHARED_LIBS)
+#define C10_EXPORT __declspec(dllexport)
+#define C10_IMPORT __declspec(dllimport)
+#else
+#define C10_EXPORT
+#define C10_IMPORT
+#endif
+#else // _WIN32
+#if defined(__GNUC__)
+#define C10_EXPORT __attribute__((__visibility__("default")))
+#define C10_HIDDEN __attribute__((__visibility__("hidden")))
+#else // defined(__GNUC__)
+#define C10_EXPORT
+#define C10_HIDDEN
+#endif // defined(__GNUC__)
+#define C10_IMPORT C10_EXPORT
+#endif // _WIN32
+
+#ifdef NO_EXPORT
+#undef C10_EXPORT
+#define C10_EXPORT
+#endif
+
+// Definition of an adaptive XX_API macro, that depends on whether you are
+// building the library itself or not, routes to XX_EXPORT and XX_IMPORT.
+// Basically, you will need to do this for each shared library that you are
+// building, and the instruction is as follows: assuming that you are building
+// a library called libawesome.so. You should:
+// (1) for your cmake target (usually done by "add_library(awesome, ...)"),
+//     define a macro called AWESOME_BUILD_MAIN_LIB using
+//     target_compile_options.
+// (2) define the AWESOME_API macro similar to the one below.
+// And in the source file of your awesome library, use AWESOME_API to
+// annotate public symbols.
+
+// Here, for the C10 library, we will define the macro C10_API for both import
+// and export.
+
+// This one is being used by libc10.so
+#ifdef C10_BUILD_MAIN_LIB
+#define C10_API C10_EXPORT
+#else
+#define C10_API C10_IMPORT
+#endif
+
+// This one is being used by libtorch.so
+#ifdef CAFFE2_BUILD_MAIN_LIB
+#define TORCH_API C10_EXPORT
+#else
+#define TORCH_API C10_IMPORT
+#endif
+
+// You may be wondering: Whose brilliant idea was it to split torch_cuda into
+// two pieces with confusing names?
+// Once upon a time, there _was_ only TORCH_CUDA_API. All was happy until we
+// tried to compile PyTorch for CUDA 11.1, which ran into relocation marker
+// issues when linking big binaries.
+// (https://github.com/pytorch/pytorch/issues/39968) We had two choices:
+//    (1) Stop supporting so many GPU architectures
+//    (2) Do something else
+// We chose #2 and decided to split the behemoth that was torch_cuda into two
+// smaller libraries, one with most of the core kernel functions (torch_cuda_cu)
+// and the other that had..well..everything else (torch_cuda_cpp). The idea was
+// this: instead of linking our static libraries (like the hefty
+// libcudnn_static.a) with another huge library, torch_cuda, and run into pesky
+// relocation marker issues, we could link our static libraries to a smaller
+// part of torch_cuda (torch_cuda_cpp) and avoid the issues.
+
+// libtorch_cuda_cu.so
+#ifdef TORCH_CUDA_CU_BUILD_MAIN_LIB
+#define TORCH_CUDA_CU_API C10_EXPORT
+#elif defined(BUILD_SPLIT_CUDA)
+#define TORCH_CUDA_CU_API C10_IMPORT
+#endif
+
+// libtorch_cuda_cpp.so
+#ifdef TORCH_CUDA_CPP_BUILD_MAIN_LIB
+#define TORCH_CUDA_CPP_API C10_EXPORT
+#elif defined(BUILD_SPLIT_CUDA)
+#define TORCH_CUDA_CPP_API C10_IMPORT
+#endif
+
+// libtorch_cuda.so (where torch_cuda_cu and torch_cuda_cpp are a part of the
+// same api)
+#ifdef TORCH_CUDA_BUILD_MAIN_LIB
+#define TORCH_CUDA_CPP_API C10_EXPORT
+#define TORCH_CUDA_CU_API C10_EXPORT
+#elif !defined(BUILD_SPLIT_CUDA)
+#define TORCH_CUDA_CPP_API C10_IMPORT
+#define TORCH_CUDA_CU_API C10_IMPORT
+#endif
+
+#if defined(TORCH_HIP_BUILD_MAIN_LIB)
+#define TORCH_HIP_API C10_EXPORT
+#else
+#define TORCH_HIP_API C10_IMPORT
+#endif
+
+#if defined(TORCH_XPU_BUILD_MAIN_LIB)
+#define TORCH_XPU_API C10_EXPORT
+#else
+#define TORCH_XPU_API C10_IMPORT
+#endif
+
+// Enums only need to be exported on windows for non-CUDA files
+#if defined(_WIN32) && defined(__CUDACC__)
+#define C10_API_ENUM C10_API
+#else
+#define C10_API_ENUM
+#endif
+
+#endif // C10_MACROS_MACROS_H_