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videochat2/lib/python3.10/site-packages/torch/include/ATen/SequenceNumber.h

@@ -0,0 +1,13 @@
+#pragma once
+
+#include <c10/macros/Export.h>
+#include <cstdint>
+
+// A simple thread local enumeration, used to link forward and backward pass
+// ops and is used by autograd and observers framework
+namespace at::sequence_number {
+
+TORCH_API uint64_t peek();
+TORCH_API uint64_t get_and_increment();
+
+} // namespace at::sequence_number

videochat2/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

videochat2/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

videochat2/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

videochat2/lib/python3.10/site-packages/torch/include/c10/core/ScalarType.h

@@ -0,0 +1,573 @@
+#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>
+#include <unordered_map>
+
+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);
+}
+
+// Returns a pair of strings representing the names for each dtype.
+// The returned pair is (name, legacy_name_if_applicable)
+C10_API std::pair<std::string, std::string> getDtypeNames(
+    c10::ScalarType scalarType);
+
+// Returns a map of string name to dtype.
+C10_API const std::unordered_map<std::string, ScalarType>& getStringToDtypeMap();
+
+} // namespace c10

videochat2/lib/python3.10/site-packages/torch/include/c10/core/SymbolicShapeMeta.h

@@ -0,0 +1,216 @@
+#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);
+  SymbolicShapeMeta& operator=(const SymbolicShapeMeta& other) = delete;
+  SymbolicShapeMeta& operator=(SymbolicShapeMeta&& other) = delete;
+
+  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

videochat2/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

videochat2/lib/python3.10/site-packages/torch/include/c10/core/impl/InlineStreamGuard.h

@@ -0,0 +1,256 @@
+#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(std::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.
+  std::optional<Stream> original_stream() const {
+    return guard_.has_value() ? std::make_optional(guard_->original_stream())
+                              : std::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.
+  std::optional<Stream> current_stream() const {
+    return guard_.has_value() ? std::make_optional(guard_->current_stream())
+                              : std::nullopt;
+  }
+
+  /// Restore the original device and stream, resetting this guard to
+  /// uninitialized state.
+  void reset() {
+    guard_.reset();
+  }
+
+ private:
+  std::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:
+  std::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

videochat2/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

videochat2/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/python_stub.h>
+#include <optional>
+
+#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 std::nullopt;
+    } else if (interpreter == self_interpreter) {
+      // NB: pyobj_ could still be null!
+      if (!ignore_hermetic_tls && c10::impl::HermeticPyObjectTLS::get_state()) {
+        return std::nullopt;
+      } else {
+        return std::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

videochat2/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

videochat2/lib/python3.10/site-packages/torch/include/c10/cuda/CUDAAlgorithm.h

@@ -0,0 +1,31 @@
+#ifdef THRUST_DEVICE_LOWER_BOUND_WORKS
+#include <thrust/binary_search.h>
+#include <thrust/device_vector.h>
+#include <thrust/execution_policy.h>
+#include <thrust/functional.h>
+#endif
+namespace c10::cuda {
+#ifdef THRUST_DEVICE_LOWER_BOUND_WORKS
+template <typename Iter, typename Scalar>
+__forceinline__ __device__ Iter
+lower_bound(Iter start, Iter end, Scalar value) {
+  return thrust::lower_bound(thrust::device, start, end, value);
+}
+#else
+// thrust::lower_bound is broken on device, see
+// https://github.com/NVIDIA/thrust/issues/1734 Implementation inspired by
+// https://github.com/pytorch/pytorch/blob/805120ab572efef66425c9f595d9c6c464383336/aten/src/ATen/native/cuda/Bucketization.cu#L28
+template <typename Iter, typename Scalar>
+__device__ Iter lower_bound(Iter start, Iter end, Scalar value) {
+  while (start < end) {
+    auto mid = start + ((end - start) >> 1);
+    if (*mid < value) {
+      start = mid + 1;
+    } else {
+      end = mid;
+    }
+  }
+  return end;
+}
+#endif // THRUST_DEVICE_LOWER_BOUND_WORKS
+} // namespace c10::cuda

videochat2/lib/python3.10/site-packages/torch/include/c10/cuda/CUDACachingAllocator.h

@@ -0,0 +1,499 @@
+#pragma once
+
+#include <c10/core/CachingDeviceAllocator.h>
+#include <c10/cuda/CUDAGraphsC10Utils.h>
+#include <c10/cuda/CUDAMacros.h>
+#include <c10/cuda/CUDAStream.h>
+#include <c10/util/ApproximateClock.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Registry.h>
+
+#include <array>
+#include <atomic>
+#include <cstddef>
+#include <cstdint>
+#include <functional>
+#include <memory>
+#include <string>
+#include <unordered_set>
+#include <utility>
+
+namespace c10 {
+
+// Caching allocator will execute every registered callback if it unable to find
+// block inside of already allocated area.
+class C10_CUDA_API FreeMemoryCallback {
+ public:
+  virtual ~FreeMemoryCallback() = default;
+  virtual bool Execute() = 0;
+};
+
+C10_DECLARE_REGISTRY(FreeCudaMemoryCallbacksRegistry, FreeMemoryCallback);
+#define REGISTER_FREE_MEMORY_CALLBACK(name, ...) \
+  C10_REGISTER_CLASS(FreeCudaMemoryCallbacksRegistry, name, __VA_ARGS__);
+} // namespace c10
+  //
+// TODO: Turn this into an honest to goodness class. I briefly attempted to do
+// this, but it was a bit irritating to figure out how to also correctly
+// apply pimpl pattern so I didn't have to leak any internal implementation
+// details in the header (CUDACachingAllocator could be made a pimpl, but
+// you also need to appropriately define a class which is a subclass
+// of Allocator. Not impossible, but required a bit more surgery than
+// I wanted to do at the time.)
+//
+// Why is this using a namespace rather than old-style THCCachingAllocator_
+// prefix?  Mostly because it made the HIPify rules easier to write; _ is
+// not counted as a word boundary, so you would otherwise have to list each
+// of these functions.
+
+namespace c10::cuda::CUDACachingAllocator {
+
+// Preserved only for BC reasons
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using c10::CachingDeviceAllocator::DeviceStats;
+
+extern const size_t kLargeBuffer;
+
+typedef std::shared_ptr<GatheredContext> (*CreateContextFn)();
+
+// Struct containing info of an allocation block (i.e. a fractional part of a
+// cudaMalloc)..
+struct BlockInfo {
+  size_t size = 0;
+  size_t requested_size = 0;
+  int32_t gc_counter = 0;
+  bool allocated = false;
+  bool active = false;
+  std::shared_ptr<GatheredContext>
+      context_when_allocated; // per-watcher context
+};
+
+// Struct containing info of a memory segment (i.e. one contiguous cudaMalloc).
+struct SegmentInfo {
+  c10::DeviceIndex device = 0;
+  size_t address = 0;
+  size_t total_size = 0;
+  size_t requested_size = 0; // unrounded, actually requested size
+  size_t allocated_size = 0;
+  size_t active_size = 0;
+  cudaStream_t stream = nullptr;
+  bool is_large = false;
+  bool is_expandable = false;
+  MempoolId_t owner_private_pool_id = {0, 0};
+  std::vector<BlockInfo> blocks;
+  std::shared_ptr<GatheredContext> context_when_allocated;
+};
+
+struct AllocatorState {
+  virtual ~AllocatorState() = default;
+};
+
+union trace_time_ {
+  time_t t_;
+  approx_time_t approx_t_;
+};
+
+struct TraceEntry {
+  enum Action {
+    ALLOC, // API made to the caching allocator for new memory
+    FREE_REQUESTED, // API call made to the caching allocator to free memory
+    FREE_COMPLETED, // The allocator might have to delay a free because
+                    // it is still in use on another stream via record_stream
+                    // This event is generated when a free actually completes.
+    SEGMENT_ALLOC, // a call to cudaMalloc to get more memory from the OS
+    SEGMENT_FREE, // a call to cudaFree to return memory to the OS (e.g. to
+                  // defragment or empty_caches)
+    SEGMENT_MAP, // a call to cuMemMap (used with expandable_segments)
+    SEGMENT_UNMAP, // unmap part of a segment (used with expandable segments)
+    SNAPSHOT, // a call to snapshot, used to correlate memory snapshots to trace
+              // events
+    OOM // the allocator threw an OutOfMemoryError (addr_ is the amount of free
+        // bytes reported by cuda)
+  };
+  TraceEntry(
+      Action action,
+      c10::DeviceIndex device,
+      size_t addr,
+      size_t size,
+      cudaStream_t stream,
+      approx_time_t time,
+      std::shared_ptr<GatheredContext> context = nullptr)
+      : action_(action),
+        device_(device),
+        addr_(addr),
+        context_(std::move(context)),
+        stream_(stream),
+        size_(size) {
+    time_.approx_t_ = time;
+  }
+  Action action_;
+  c10::DeviceIndex device_;
+  size_t addr_; // for OOM, this is the amount of free bytes reported by cuda
+  std::shared_ptr<GatheredContext> context_;
+  cudaStream_t stream_{};
+  size_t size_;
+  trace_time_ time_{};
+};
+
+// Calls made by record_function will save annotations
+struct AnnotationEntry {
+  AnnotationEntry(c10::DeviceIndex device, approx_time_t time)
+      : device_(device) {
+    time_.approx_t_ = time;
+  }
+
+  void recordUserMetadata(const std::string& name, std::string value) {
+    metadata_[name] = std::move(value);
+  }
+
+  c10::DeviceIndex device_;
+  trace_time_ time_{};
+  std::unordered_map<std::string, std::string> metadata_;
+};
+
+struct AllocatorConfigInfo {
+  double garbage_collection_threshold;
+  size_t max_split_size;
+  size_t pinned_num_register_threads;
+  bool expandable_segments;
+  bool release_lock_on_malloc;
+  bool pinned_use_host_register;
+  std::string last_allocator_settings;
+  std::vector<size_t> roundup_power2_divisions;
+};
+
+struct SnapshotInfo {
+  std::vector<SegmentInfo> segments;
+  std::vector<std::vector<TraceEntry>> device_traces;
+  std::vector<AnnotationEntry> external_annotations;
+  AllocatorConfigInfo config_metadata;
+};
+
+// returns the pointers freed in the pool
+// and the pointers allocated. Note: a pointer
+// may appear in both freed and allocated
+struct CheckpointDelta {
+  std::vector<void*> ptrs_freed;
+  std::vector<at::DataPtr> dataptrs_allocd;
+};
+
+enum struct RecordContext {
+  NEVER = 0,
+  STATE = 1, // only keep stacks for active allocations
+  ALLOC = 2, // additionally keep stacks for allocations in the trace history
+  ALL = 3, // additionally record stacks for when something is freed
+};
+
+using OutOfMemoryObserver = std::function<void(
+    int64_t device,
+    size_t allocated,
+    size_t device_total,
+    size_t device_free)>;
+
+using AllocatorTraceTracker = std::function<void(const TraceEntry&)>;
+
+struct ShareableHandle {
+  ptrdiff_t offset;
+  std::string handle;
+};
+
+class CUDAAllocator : public Allocator {
+ public:
+  virtual void* raw_alloc(size_t nbytes) = 0;
+  virtual void* raw_alloc_with_stream(size_t nbytes, cudaStream_t stream) = 0;
+  virtual void raw_delete(void* ptr) = 0;
+  virtual void init(int device_count) = 0;
+  virtual bool initialized() = 0;
+  virtual void setMemoryFraction(double fraction, c10::DeviceIndex device) = 0;
+  virtual void emptyCache() = 0;
+  virtual void cacheInfo(c10::DeviceIndex device, size_t* largestBlock) = 0;
+  virtual void* getBaseAllocation(void* ptr, size_t* size) = 0;
+  virtual void recordStream(const DataPtr&, CUDAStream stream) = 0;
+  virtual c10::CachingDeviceAllocator::DeviceStats getDeviceStats(
+      c10::DeviceIndex device) = 0;
+  virtual void resetAccumulatedStats(c10::DeviceIndex device) = 0;
+  virtual void resetPeakStats(c10::DeviceIndex device) = 0;
+  virtual SnapshotInfo snapshot() = 0;
+  virtual void beginAllocateToPool(
+      c10::DeviceIndex device,
+      MempoolId_t mempool_id,
+      std::function<bool(cudaStream_t)> filter) = 0;
+  virtual void endAllocateToPool(
+      c10::DeviceIndex device,
+      MempoolId_t mempool_id) = 0;
+  virtual void releasePool(c10::DeviceIndex device, MempoolId_t mempool_id) = 0;
+  // returns true if the allocated blocks are equal to expected live allocations
+  virtual bool checkPoolLiveAllocations(
+      c10::DeviceIndex device,
+      MempoolId_t mempool_id,
+      const std::unordered_set<void*>& expected_live_allocations) {
+    TORCH_CHECK(
+        false,
+        name(),
+        " does not yet support checkPoolLiveAllocations. "
+        "If you need it, please file an issue describing your use case.");
+  }
+  virtual ShareableHandle shareIpcHandle(void* ptr) = 0;
+  virtual std::shared_ptr<void> getIpcDevPtr(std::string handle) = 0;
+  virtual bool isHistoryEnabled() {
+    TORCH_CHECK(
+        false,
+        name(),
+        " does not yet support recordHistory. "
+        "If you need it, please file an issue describing your use case.");
+  }
+  virtual void recordHistory(
+      bool enabled,
+      CreateContextFn context_recorder,
+      size_t alloc_trace_max_entries,
+      RecordContext when) = 0;
+  virtual void recordAnnotation(
+      const std::vector<std::pair<std::string, std::string>>& md){};
+  virtual void attachOutOfMemoryObserver(OutOfMemoryObserver observer) = 0;
+
+  // Attached AllocatorTraceTracker callbacks will be called while the
+  // per-device allocator lock is held. Any additional locks taken from within
+  // the callback must be proven to always have the lock order that never
+  // triggers a deadlock. In particular, Python's GIL may be held when
+  // calling the allocator so it is unsafe to try to acquire the GIL in this
+  // callback.
+  virtual void attachAllocatorTraceTracker(AllocatorTraceTracker tracker) = 0;
+
+  virtual void enablePeerAccess(
+      c10::DeviceIndex dev,
+      c10::DeviceIndex dev_to_access) = 0;
+
+  // memory not allocated from cudaMalloc cannot be copied
+  // across devices using cudaMemcpyAsync if peer to peer access is disabled.
+  // instead it requires cudaMemcpyAsyncPeer
+  //  with P2P Enabled, all combinations work
+  //  with P2P Disabled:
+  //                       cudaMalloc cudaMallocAsync/cuMemMap
+  // cudaMemcpyAsyncPeer   works      works
+  // cudaMemcpyAsync       works      error
+
+  // This function performs chooses to use the Peer version of
+  // memcpy if required based on where the allocated put dst/src.
+  virtual cudaError_t memcpyAsync(
+      void* dst,
+      int dstDevice,
+      const void* src,
+      int srcDevice,
+      size_t count,
+      cudaStream_t stream,
+      bool p2p_enabled) = 0;
+  virtual std::shared_ptr<AllocatorState> getCheckpointState(
+      c10::DeviceIndex device,
+      MempoolId_t id) = 0;
+  virtual CheckpointDelta setCheckpointPoolState(
+      c10::DeviceIndex device,
+      std::shared_ptr<AllocatorState> pps) = 0;
+  virtual std::string name() = 0;
+};
+
+// Allocator object, statically initialized
+// See BackendInitializer in CUDACachingAllocator.cpp.
+// Atomic loads on x86 are just normal loads,
+// (atomic stores are different), so reading this value
+// is no different than loading a pointer.
+C10_CUDA_API extern std::atomic<CUDAAllocator*> allocator;
+
+inline CUDAAllocator* get() {
+  return allocator.load();
+}
+
+// Called directly by clients.
+inline void* raw_alloc(size_t nbytes) {
+  return get()->raw_alloc(nbytes);
+}
+
+inline void* raw_alloc_with_stream(size_t nbytes, cudaStream_t stream) {
+  return get()->raw_alloc_with_stream(nbytes, stream);
+}
+
+inline void raw_delete(void* ptr) {
+  return get()->raw_delete(ptr);
+}
+
+inline void init(int device_count) {
+  return get()->init(device_count);
+}
+
+inline void setMemoryFraction(double fraction, c10::DeviceIndex device) {
+  return get()->setMemoryFraction(fraction, device);
+}
+
+inline void emptyCache() {
+  return get()->emptyCache();
+}
+
+inline void cacheInfo(c10::DeviceIndex device, size_t* largestBlock) {
+  return get()->cacheInfo(device, largestBlock);
+}
+
+inline void* getBaseAllocation(void* ptr, size_t* size) {
+  return get()->getBaseAllocation(ptr, size);
+}
+
+inline void recordStream(const DataPtr& dataPtr, CUDAStream stream) {
+  return get()->recordStream(dataPtr, stream);
+}
+
+inline c10::CachingDeviceAllocator::DeviceStats getDeviceStats(
+    c10::DeviceIndex device) {
+  return get()->getDeviceStats(device);
+}
+
+inline void resetAccumulatedStats(c10::DeviceIndex device) {
+  return get()->resetAccumulatedStats(device);
+}
+
+inline void resetPeakStats(c10::DeviceIndex device) {
+  return get()->resetPeakStats(device);
+}
+
+inline SnapshotInfo snapshot() {
+  return get()->snapshot();
+}
+
+inline std::shared_ptr<AllocatorState> getCheckpointState(
+    c10::DeviceIndex device,
+    MempoolId_t id) {
+  return get()->getCheckpointState(device, id);
+}
+
+inline CheckpointDelta setCheckpointPoolState(
+    c10::DeviceIndex device,
+    std::shared_ptr<AllocatorState> pps) {
+  return get()->setCheckpointPoolState(device, std::move(pps));
+}
+
+// CUDAGraph interactions
+inline void beginAllocateToPool(
+    c10::DeviceIndex device,
+    MempoolId_t mempool_id,
+    std::function<bool(cudaStream_t)> filter) {
+  get()->beginAllocateToPool(device, mempool_id, std::move(filter));
+}
+
+inline void endAllocateToPool(c10::DeviceIndex device, MempoolId_t mempool_id) {
+  get()->endAllocateToPool(device, mempool_id);
+}
+
+inline void recordHistory(
+    bool enabled,
+    CreateContextFn context_recorder,
+    size_t alloc_trace_max_entries,
+    RecordContext when) {
+  return get()->recordHistory(
+      enabled, context_recorder, alloc_trace_max_entries, when);
+}
+
+inline void recordAnnotation(
+    const std::vector<std::pair<std::string, std::string>>& md) {
+  return get()->recordAnnotation(md);
+}
+
+inline bool isHistoryEnabled() {
+  return get()->isHistoryEnabled();
+}
+
+inline bool checkPoolLiveAllocations(
+    c10::DeviceIndex device,
+    MempoolId_t mempool_id,
+    const std::unordered_set<void*>& expected_live_allocations) {
+  return get()->checkPoolLiveAllocations(
+      device, mempool_id, expected_live_allocations);
+}
+
+inline void attachOutOfMemoryObserver(OutOfMemoryObserver observer) {
+  return get()->attachOutOfMemoryObserver(std::move(observer));
+}
+
+inline void attachAllocatorTraceTracker(AllocatorTraceTracker tracker) {
+  return get()->attachAllocatorTraceTracker(std::move(tracker));
+}
+
+inline void releasePool(c10::DeviceIndex device, MempoolId_t mempool_id) {
+  return get()->releasePool(device, mempool_id);
+}
+// Not part of CUDA_ALLOCATOR_BACKEND_INTERFACE
+inline std::shared_ptr<void> getIpcDevPtr(std::string handle) {
+  return get()->getIpcDevPtr(std::move(handle));
+}
+
+inline ShareableHandle shareIpcHandle(void* ptr) {
+  return get()->shareIpcHandle(ptr);
+}
+
+inline std::string name() {
+  return get()->name();
+}
+
+inline cudaError_t memcpyAsync(
+    void* dst,
+    int dstDevice,
+    const void* src,
+    int srcDevice,
+    size_t count,
+    cudaStream_t stream,
+    bool p2p_enabled) {
+  return get()->memcpyAsync(
+      dst, dstDevice, src, srcDevice, count, stream, p2p_enabled);
+}
+
+inline void enablePeerAccess(
+    c10::DeviceIndex dev,
+    c10::DeviceIndex dev_to_access) {
+  return get()->enablePeerAccess(dev, dev_to_access);
+}
+
+} // namespace c10::cuda::CUDACachingAllocator
+
+namespace c10::cuda {
+
+// MemPool represents a pool of memory in a caching allocator. Currently,
+// it's just the ID of the pool object maintained in the CUDACachingAllocator.
+//
+// An allocator pointer can be passed to the MemPool to define how the
+// allocations should be done in the pool. For example: using a different
+// system allocator such as ncclMemAlloc.
+struct C10_CUDA_API MemPool {
+  MemPool(
+      CUDACachingAllocator::CUDAAllocator* allocator = nullptr,
+      bool is_user_created = true);
+
+  MempoolId_t id();
+  CUDACachingAllocator::CUDAAllocator* allocator();
+
+ private:
+  static std::atomic<CaptureId_t> uid_;
+  static std::atomic<CaptureId_t> uuid_;
+  CUDACachingAllocator::CUDAAllocator* allocator_;
+  bool is_user_created_;
+  MempoolId_t id_;
+};
+
+// MemPoolContext holds the currently active pool and stashes the previous
+// pool. On deletion it makes the previous pool active.
+struct C10_CUDA_API MemPoolContext {
+  MemPoolContext(MemPool* mempool);
+
+  ~MemPoolContext();
+
+  // getActiveMemPool() can be used to get the currently active pool.
+  // For instance: in CUDACachingAllocator, we can route allocations
+  // to a user provided allocator, by doing:
+  //
+  //  auto active_pool = MemPoolContext::getActiveMemPool();
+  //  if (active_pool && active_pool->allocator()) {
+  //    ptr = active_pool->allocator()->raw_alloc(size);
+  //  }
+  //
+  static MemPool* getActiveMemPool();
+
+ private:
+  MemPool* prev_mempool_;
+};
+
+} // namespace c10::cuda

videochat2/lib/python3.10/site-packages/torch/include/c10/cuda/CUDADeviceAssertion.h

@@ -0,0 +1,96 @@
+#pragma once
+
+#include <c10/cuda/CUDAException.h>
+#include <c10/macros/Macros.h>
+
+namespace c10::cuda {
+
+#ifdef TORCH_USE_CUDA_DSA
+// Copy string from `src` to `dst`
+static __device__ void dstrcpy(char* dst, const char* src) {
+  int i = 0;
+  // Copy string from source to destination, ensuring that it
+  // isn't longer than `C10_CUDA_DSA_MAX_STR_LEN-1`
+  while (*src != '\0' && i++ < C10_CUDA_DSA_MAX_STR_LEN - 1) {
+    *dst++ = *src++;
+  }
+  *dst = '\0';
+}
+
+static __device__ void dsa_add_new_assertion_failure(
+    DeviceAssertionsData* assertions_data,
+    const char* assertion_msg,
+    const char* filename,
+    const char* function_name,
+    const int line_number,
+    const uint32_t caller,
+    const dim3 block_id,
+    const dim3 thread_id) {
+  // `assertions_data` may be nullptr if device-side assertion checking
+  // is disabled at run-time. If it is disabled at compile time this
+  // function will never be called
+  if (!assertions_data) {
+    return;
+  }
+
+  // Atomically increment so other threads can fail at the same time
+  // Note that incrementing this means that the CPU can observe that
+  // a failure has happened and can begin to respond before we've
+  // written information about that failure out to the buffer.
+  const auto nid = atomicAdd(&(assertions_data->assertion_count), 1);
+
+  if (nid >= C10_CUDA_DSA_ASSERTION_COUNT) {
+    // At this point we're ran out of assertion buffer space.
+    // We could print a message about this, but that'd get
+    // spammy if a lot of threads did it, so we just silently
+    // ignore any other assertion failures. In most cases the
+    // failures will all probably be analogous anyway.
+    return;
+  }
+
+  // Write information about the assertion failure to memory.
+  // Note that this occurs only after the `assertion_count`
+  // increment broadcasts that there's been a problem.
+  auto& self = assertions_data->assertions[nid];
+  dstrcpy(self.assertion_msg, assertion_msg);
+  dstrcpy(self.filename, filename);
+  dstrcpy(self.function_name, function_name);
+  self.line_number = line_number;
+  self.caller = caller;
+  self.block_id[0] = block_id.x;
+  self.block_id[1] = block_id.y;
+  self.block_id[2] = block_id.z;
+  self.thread_id[0] = thread_id.x;
+  self.thread_id[1] = thread_id.y;
+  self.thread_id[2] = thread_id.z;
+}
+
+// Emulates a kernel assertion. The assertion won't stop the kernel's progress,
+// so you should assume everything the kernel produces is garbage if there's an
+// assertion failure.
+// NOTE: This assumes that `assertions_data` and  `assertion_caller_id` are
+//       arguments of the kernel and therefore accessible.
+#define CUDA_KERNEL_ASSERT2(condition)                                   \
+  do {                                                                   \
+    if (C10_UNLIKELY(!(condition))) {                                    \
+      /* Has an atomic element so threads can fail at the same time */   \
+      c10::cuda::dsa_add_new_assertion_failure(                          \
+          assertions_data,                                               \
+          C10_STRINGIZE(condition),                                      \
+          __FILE__,                                                      \
+          __FUNCTION__,                                                  \
+          __LINE__,                                                      \
+          assertion_caller_id,                                           \
+          blockIdx,                                                      \
+          threadIdx);                                                    \
+      /* Now that the kernel has failed we early exit the kernel, but */ \
+      /* otherwise keep going and rely on the host to check UVM and */   \
+      /* determine we've had a problem */                                \
+      return;                                                            \
+    }                                                                    \
+  } while (false)
+#else
+#define CUDA_KERNEL_ASSERT2(condition) assert(condition)
+#endif
+
+} // namespace c10::cuda

videochat2/lib/python3.10/site-packages/torch/include/c10/cuda/CUDADeviceAssertionHost.h

@@ -0,0 +1,164 @@
+#pragma once
+
+#include <c10/cuda/CUDAMacros.h>
+
+#include <cstdint>
+#include <memory>
+#include <mutex>
+#include <string>
+#include <utility>
+#include <vector>
+
+#ifdef USE_CUDA
+#define TORCH_USE_CUDA_DSA
+#endif
+
+/// Number of assertion failure messages we can store. If this is too small
+/// threads will fail silently.
+constexpr int C10_CUDA_DSA_ASSERTION_COUNT = 10;
+constexpr int C10_CUDA_DSA_MAX_STR_LEN = 512;
+
+namespace c10::cuda {
+
+/// Holds information about any device-side assertions that fail.
+/// Held in managed memory and access by both the CPU and the GPU.
+struct DeviceAssertionData {
+  /// Stringification of the assertion
+  // NOLINTNEXTLINE(*-c-arrays)
+  char assertion_msg[C10_CUDA_DSA_MAX_STR_LEN]{};
+  /// File the assertion was in
+  // NOLINTNEXTLINE(*-c-arrays)
+  char filename[C10_CUDA_DSA_MAX_STR_LEN]{};
+  /// Name of the function the assertion was in
+  // NOLINTNEXTLINE(*-c-arrays)
+  char function_name[C10_CUDA_DSA_MAX_STR_LEN]{};
+  /// Line number the assertion was at
+  int line_number{};
+  /// Number uniquely identifying the kernel launch that triggered the assertion
+  uint32_t caller{};
+  /// block_id of the thread that failed the assertion
+  // NOLINTNEXTLINE(*-c-arrays)
+  int32_t block_id[3]{};
+  /// third_id of the thread that failed the assertion
+  // NOLINTNEXTLINE(*-c-arrays)
+  int32_t thread_id[3]{};
+};
+
+/// Used to hold assertions generated by the device
+/// Held in managed memory and access by both the CPU and the GPU.
+struct DeviceAssertionsData {
+  /// Total number of assertions found; a subset of thse will be recorded
+  /// in `assertions`
+  int32_t assertion_count{};
+  /// An array of assertions that will be written to in a race-free manner
+  // NOLINTNEXTLINE(*-c-arrays)
+  DeviceAssertionData assertions[C10_CUDA_DSA_ASSERTION_COUNT]{};
+};
+
+/// Use to hold info about kernel launches so that we can run kernels
+/// asynchronously and still associate launches with device-side
+/// assertion failures
+struct CUDAKernelLaunchInfo {
+  /// Filename of the code where the kernel was launched from
+  const char* launch_filename;
+  /// Function from which the kernel was launched
+  const char* launch_function;
+  /// Line number of where the code was launched from
+  uint32_t launch_linenum;
+  /// Backtrace of where the kernel was launched from, only populated if
+  /// CUDAKernelLaunchRegistry::gather_launch_stacktrace is True
+  std::string launch_stacktrace;
+  /// Kernel that was launched
+  const char* kernel_name;
+  /// Device the kernel was launched on
+  int device;
+  /// Stream the kernel was launched on
+  int32_t stream;
+  /// A number that uniquely identifies the kernel launch
+  uint64_t generation_number;
+};
+
+/// Circular buffer used to hold information about kernel launches
+/// this is later used to reconstruct how a device-side kernel assertion failure
+/// occurred CUDAKernelLaunchRegistry is used as a singleton
+class C10_CUDA_API CUDAKernelLaunchRegistry {
+ private:
+  /// Assume that this is the max number of kernel launches that might ever be
+  /// enqueued across all streams on a single device
+  static constexpr int max_kernel_launches = 1024;
+  /// How many kernel launch infos we've inserted. Used to ensure that circular
+  /// queue doesn't provide false information by always increasing, but also to
+  /// mark where we are inserting into the queue
+#ifdef TORCH_USE_CUDA_DSA
+  uint64_t generation_number = 0;
+#endif
+  /// Shared mutex between writer and accessor to ensure multi-threaded safety.
+  mutable std::mutex read_write_mutex;
+  /// Used to ensure prevent race conditions in GPU memory allocation
+  mutable std::mutex gpu_alloc_mutex;
+  /// Pointer to managed memory keeping track of device-side assertions. There
+  /// is one entry for each possible device the process might work with. Unused
+  /// entries are nullptrs. We could also use an unordered_set here, but this
+  /// vector design will be faster and the wasted memory is small since we
+  /// expect the number of GPUs per node will always be small
+  std::vector<
+      std::unique_ptr<DeviceAssertionsData, void (*)(DeviceAssertionsData*)>>
+      uvm_assertions;
+  /// A single circular buffer holds information about every kernel launch the
+  /// process makes across all devices.
+  std::vector<CUDAKernelLaunchInfo> kernel_launches;
+  bool check_env_for_enable_launch_stacktracing() const;
+  bool check_env_for_dsa_enabled() const;
+
+ public:
+  CUDAKernelLaunchRegistry();
+  /// Register a new kernel launch and obtain a generation number back to be
+  /// passed to the kernel
+  uint32_t insert(
+      const char* launch_filename,
+      const char* launch_function,
+      const uint32_t launch_linenum,
+      const char* kernel_name,
+      const int32_t stream_id);
+  /// Get copies of the kernel launch registry and each device's assertion
+  /// failure buffer so they can be inspected without raising race conditions
+  std::
+      pair<std::vector<DeviceAssertionsData>, std::vector<CUDAKernelLaunchInfo>>
+      snapshot() const;
+  /// Get a pointer to the current device's assertion failure buffer. If no such
+  /// buffer exists then one is created. This means that the first kernel launch
+  /// made on each device will be slightly slower because memory allocations are
+  /// required
+  DeviceAssertionsData* get_uvm_assertions_ptr_for_current_device();
+  /// Gets the global singleton of the registry
+  static CUDAKernelLaunchRegistry& get_singleton_ref();
+  /// If not all devices support DSA, we disable it
+  const bool do_all_devices_support_managed_memory = false;
+  /// Whether or not to gather stack traces when launching kernels
+  bool gather_launch_stacktrace = false;
+  /// Whether or not host-side DSA is enabled or disabled at run-time
+  /// Note: Device-side code cannot be enabled/disabled at run-time
+  bool enabled_at_runtime = false;
+  /// Whether or not a device has indicated a failure
+  bool has_failed() const;
+#ifdef TORCH_USE_CUDA_DSA
+  const bool enabled_at_compile_time = true;
+#else
+  const bool enabled_at_compile_time = false;
+#endif
+};
+
+std::string c10_retrieve_device_side_assertion_info();
+
+} // namespace c10::cuda
+
+// Each kernel launched with TORCH_DSA_KERNEL_LAUNCH
+// requires the same input arguments. We introduce the following macro to
+// standardize these.
+#define TORCH_DSA_KERNEL_ARGS                                              \
+  [[maybe_unused]] c10::cuda::DeviceAssertionsData *const assertions_data, \
+      [[maybe_unused]] uint32_t assertion_caller_id
+
+// This macro can be used to pass the DSA arguments onward to another
+// function
+#define TORCH_DSA_KERNEL_ARGS_PASS assertions_data, assertion_caller_id

videochat2/lib/python3.10/site-packages/torch/include/c10/cuda/CUDAException.h

@@ -0,0 +1,100 @@
+#pragma once
+
+#include <c10/cuda/CUDADeviceAssertionHost.h>
+#include <c10/cuda/CUDAMacros.h>
+#include <c10/cuda/CUDAMiscFunctions.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+#include <c10/util/irange.h>
+#include <cuda.h>
+
+// Note [CHECK macro]
+// ~~~~~~~~~~~~~~~~~~
+// This is a macro so that AT_ERROR can get accurate __LINE__
+// and __FILE__ information.  We could split this into a short
+// macro and a function implementation if we pass along __LINE__
+// and __FILE__, but no one has found this worth doing.
+
+// Used to denote errors from CUDA framework.
+// This needs to be declared here instead util/Exception.h for proper conversion
+// during hipify.
+namespace c10 {
+class C10_CUDA_API CUDAError : public c10::Error {
+  using Error::Error;
+};
+} // namespace c10
+
+#define C10_CUDA_CHECK(EXPR)                                        \
+  do {                                                              \
+    const cudaError_t __err = EXPR;                                 \
+    c10::cuda::c10_cuda_check_implementation(                       \
+        static_cast<int32_t>(__err),                                \
+        __FILE__,                                                   \
+        __func__, /* Line number data type not well-defined between \
+                      compilers, so we perform an explicit cast */  \
+        static_cast<uint32_t>(__LINE__),                            \
+        true);                                                      \
+  } while (0)
+
+#define C10_CUDA_CHECK_WARN(EXPR)                              \
+  do {                                                         \
+    const cudaError_t __err = EXPR;                            \
+    if (C10_UNLIKELY(__err != cudaSuccess)) {                  \
+      auto error_unused C10_UNUSED = cudaGetLastError();       \
+      (void)error_unused;                                      \
+      TORCH_WARN("CUDA warning: ", cudaGetErrorString(__err)); \
+    }                                                          \
+  } while (0)
+
+// Indicates that a CUDA error is handled in a non-standard way
+#define C10_CUDA_ERROR_HANDLED(EXPR) EXPR
+
+// Intentionally ignore a CUDA error
+#define C10_CUDA_IGNORE_ERROR(EXPR)                             \
+  do {                                                          \
+    const cudaError_t __err = EXPR;                             \
+    if (C10_UNLIKELY(__err != cudaSuccess)) {                   \
+      cudaError_t error_unused C10_UNUSED = cudaGetLastError(); \
+      (void)error_unused;                                       \
+    }                                                           \
+  } while (0)
+
+// Clear the last CUDA error
+#define C10_CUDA_CLEAR_ERROR()                                \
+  do {                                                        \
+    cudaError_t error_unused C10_UNUSED = cudaGetLastError(); \
+    (void)error_unused;                                       \
+  } while (0)
+
+// This should be used directly after every kernel launch to ensure
+// the launch happened correctly and provide an early, close-to-source
+// diagnostic if it didn't.
+#define C10_CUDA_KERNEL_LAUNCH_CHECK() C10_CUDA_CHECK(cudaGetLastError())
+
+/// Launches a CUDA kernel appending to it all the information need to handle
+/// device-side assertion failures. Checks that the launch was successful.
+#define TORCH_DSA_KERNEL_LAUNCH(                                      \
+    kernel, blocks, threads, shared_mem, stream, ...)                 \
+  do {                                                                \
+    auto& launch_registry =                                           \
+        c10::cuda::CUDAKernelLaunchRegistry::get_singleton_ref();     \
+    kernel<<<blocks, threads, shared_mem, stream>>>(                  \
+        __VA_ARGS__,                                                  \
+        launch_registry.get_uvm_assertions_ptr_for_current_device(),  \
+        launch_registry.insert(                                       \
+            __FILE__, __FUNCTION__, __LINE__, #kernel, stream.id())); \
+    C10_CUDA_KERNEL_LAUNCH_CHECK();                                   \
+  } while (0)
+
+namespace c10::cuda {
+
+/// In the event of a CUDA failure, formats a nice error message about that
+/// failure and also checks for device-side assertion failures
+C10_CUDA_API void c10_cuda_check_implementation(
+    const int32_t err,
+    const char* filename,
+    const char* function_name,
+    const int line_number,
+    const bool include_device_assertions);
+
+} // namespace c10::cuda

videochat2/lib/python3.10/site-packages/torch/include/c10/cuda/CUDAMacros.h

@@ -0,0 +1,51 @@
+#pragma once
+
+#ifndef C10_USING_CUSTOM_GENERATED_MACROS
+
+// We have not yet modified the AMD HIP build to generate this file so
+// we add an extra option to specifically ignore it.
+#ifndef C10_CUDA_NO_CMAKE_CONFIGURE_FILE
+#include <c10/cuda/impl/cuda_cmake_macros.h>
+#endif // C10_CUDA_NO_CMAKE_CONFIGURE_FILE
+
+#endif
+
+// See c10/macros/Export.h for a detailed explanation of what the function
+// of these macros are.  We need one set of macros for every separate library
+// we build.
+
+#ifdef _WIN32
+#if defined(C10_CUDA_BUILD_SHARED_LIBS)
+#define C10_CUDA_EXPORT __declspec(dllexport)
+#define C10_CUDA_IMPORT __declspec(dllimport)
+#else
+#define C10_CUDA_EXPORT
+#define C10_CUDA_IMPORT
+#endif
+#else // _WIN32
+#if defined(__GNUC__)
+#define C10_CUDA_EXPORT __attribute__((__visibility__("default")))
+#else // defined(__GNUC__)
+#define C10_CUDA_EXPORT
+#endif // defined(__GNUC__)
+#define C10_CUDA_IMPORT C10_CUDA_EXPORT
+#endif // _WIN32
+
+// This one is being used by libc10_cuda.so
+#ifdef C10_CUDA_BUILD_MAIN_LIB
+#define C10_CUDA_API C10_CUDA_EXPORT
+#else
+#define C10_CUDA_API C10_CUDA_IMPORT
+#endif
+
+/**
+ * The maximum number of GPUs that we recognizes. Increasing this beyond the
+ * initial limit of 16 broke Caffe2 testing, hence the ifdef guards.
+ * This value cannot be more than 128 because our DeviceIndex is a uint8_t.
+o */
+#ifdef FBCODE_CAFFE2
+// fbcode depends on this value being 16
+#define C10_COMPILE_TIME_MAX_GPUS 16
+#else
+#define C10_COMPILE_TIME_MAX_GPUS 120
+#endif

videochat2/lib/python3.10/site-packages/torch/include/c10/cuda/CUDAMiscFunctions.h

@@ -0,0 +1,12 @@
+#pragma once
+// this file is to avoid circular dependency between CUDAFunctions.h and
+// CUDAExceptions.h
+
+#include <c10/cuda/CUDAMacros.h>
+
+#include <mutex>
+
+namespace c10::cuda {
+C10_CUDA_API const char* get_cuda_check_suffix() noexcept;
+C10_CUDA_API std::mutex* getFreeMutex();
+} // namespace c10::cuda

videochat2/lib/python3.10/site-packages/torch/include/c10/cuda/impl/CUDAGuardImpl.h

@@ -0,0 +1,249 @@
+#pragma once
+
+#include <c10/core/impl/DeviceGuardImplInterface.h>
+#include <c10/core/impl/GPUTrace.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+
+#include <c10/cuda/CUDACachingAllocator.h>
+#include <c10/cuda/CUDAException.h>
+#include <c10/cuda/CUDAFunctions.h>
+#include <c10/cuda/CUDAStream.h>
+
+#include <c10/core/Device.h>
+#include <c10/core/DeviceType.h>
+#include <c10/core/Stream.h>
+#include <c10/core/impl/PyInterpreter.h>
+#include <cuda_runtime_api.h>
+#include <cstdint>
+#include <optional>
+
+namespace c10::cuda::impl {
+
+struct CUDAGuardImpl final : public c10::impl::DeviceGuardImplInterface {
+  static constexpr DeviceType static_type = DeviceType::CUDA;
+
+  CUDAGuardImpl() = default;
+  explicit CUDAGuardImpl(DeviceType t) {
+    TORCH_INTERNAL_ASSERT(t == DeviceType::CUDA);
+  }
+  DeviceType type() const override {
+    return DeviceType::CUDA;
+  }
+  Device exchangeDevice(Device d) const override {
+    TORCH_INTERNAL_ASSERT(d.is_cuda());
+    auto old_device_index = c10::cuda::ExchangeDevice(d.index());
+    return Device(DeviceType::CUDA, old_device_index);
+  }
+  Device getDevice() const override {
+    DeviceIndex device = 0;
+    C10_CUDA_CHECK(c10::cuda::GetDevice(&device));
+    return Device(DeviceType::CUDA, device);
+  }
+  std::optional<Device> uncheckedGetDevice() const noexcept {
+    DeviceIndex device{-1};
+    const auto err = C10_CUDA_ERROR_HANDLED(c10::cuda::GetDevice(&device));
+    C10_CUDA_CHECK_WARN(err);
+    if (err != cudaSuccess) {
+      return std::nullopt;
+    }
+    return Device(DeviceType::CUDA, device);
+  }
+  void setDevice(Device d) const override {
+    TORCH_INTERNAL_ASSERT(d.is_cuda());
+    C10_CUDA_CHECK(c10::cuda::SetDevice(d.index()));
+  }
+  void uncheckedSetDevice(Device d) const noexcept override {
+    C10_CUDA_CHECK_WARN(c10::cuda::MaybeSetDevice(d.index()));
+  }
+  Stream getStream(Device d) const noexcept override {
+    return getCurrentCUDAStream(d.index()).unwrap();
+  }
+  Stream getDefaultStream(Device d) const override {
+    return getDefaultCUDAStream(d.index());
+  }
+  Stream getNewStream(Device d, int priority = 0) const override {
+    return getStreamFromPool(priority, d.index());
+  }
+  Stream getStreamFromGlobalPool(Device d, bool isHighPriority = false)
+      const override {
+    return getStreamFromPool(isHighPriority, d.index());
+  }
+  // NB: These do NOT set the current device
+  Stream exchangeStream(Stream s) const noexcept override {
+    CUDAStream cs(s);
+    auto old_stream = getCurrentCUDAStream(s.device().index());
+    setCurrentCUDAStream(cs);
+    return old_stream.unwrap();
+  }
+  DeviceIndex deviceCount() const noexcept override {
+    return device_count();
+  }
+
+  // Event-related functions
+  void createEvent(cudaEvent_t* cuda_event, const EventFlag flag) const {
+    // Maps PyTorch's Event::Flag to CUDA flag
+    auto cuda_flag = cudaEventDefault;
+    switch (flag) {
+      case EventFlag::PYTORCH_DEFAULT:
+        cuda_flag = cudaEventDisableTiming;
+        break;
+      case EventFlag::BACKEND_DEFAULT:
+        cuda_flag = cudaEventDefault;
+        break;
+      default:
+        TORCH_CHECK(false, "CUDA event received unknown flag");
+    }
+
+    C10_CUDA_CHECK(cudaEventCreateWithFlags(cuda_event, cuda_flag));
+    const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+    if (C10_UNLIKELY(interp)) {
+      (*interp)->trace_gpu_event_creation(
+          c10::kCUDA, reinterpret_cast<uintptr_t>(cuda_event));
+    }
+  }
+
+  void destroyEvent(void* event, const DeviceIndex device_index)
+      const noexcept override {
+    if (!event)
+      return;
+    auto cuda_event = static_cast<cudaEvent_t>(event);
+    DeviceIndex orig_device{-1};
+    C10_CUDA_CHECK_WARN(c10::cuda::GetDevice(&orig_device));
+    C10_CUDA_CHECK_WARN(c10::cuda::SetDevice(device_index));
+    const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+    if (C10_UNLIKELY(interp)) {
+      (*interp)->trace_gpu_event_deletion(
+          c10::kCUDA, reinterpret_cast<uintptr_t>(cuda_event));
+    }
+    C10_CUDA_CHECK_WARN(cudaEventDestroy(cuda_event));
+    C10_CUDA_CHECK_WARN(c10::cuda::SetDevice(orig_device));
+  }
+
+  void record(
+      void** event,
+      const Stream& stream,
+      const DeviceIndex device_index,
+      const EventFlag flag) const override {
+    TORCH_CHECK(
+        device_index == -1 || device_index == stream.device_index(),
+        "Event device index ",
+        device_index,
+        " does not match recording stream's device index ",
+        stream.device_index(),
+        ".");
+
+    cudaEvent_t cuda_event = static_cast<cudaEvent_t>(*event);
+    CUDAStream cuda_stream{stream};
+
+    // Moves to stream's device to record
+    const auto orig_device = getDevice();
+    setDevice(stream.device());
+
+    // Creates the event (lazily)
+    if (!cuda_event)
+      createEvent(&cuda_event, flag);
+    C10_CUDA_CHECK(cudaEventRecord(cuda_event, cuda_stream));
+    // Makes the void* point to the (possibly just allocated) CUDA event
+    *event = cuda_event;
+    const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+    if (C10_UNLIKELY(interp)) {
+      (*interp)->trace_gpu_event_record(
+          c10::kCUDA,
+          reinterpret_cast<uintptr_t>(cuda_event),
+          reinterpret_cast<uintptr_t>(cuda_stream.stream()));
+    }
+
+    // Resets device
+    setDevice(orig_device);
+  }
+
+  void block(void* event, const Stream& stream) const override {
+    if (!event)
+      return;
+    cudaEvent_t cuda_event = static_cast<cudaEvent_t>(event);
+    CUDAStream cuda_stream{stream};
+    const auto orig_device = getDevice();
+    setDevice(stream.device());
+    C10_CUDA_CHECK(cudaStreamWaitEvent(
+        cuda_stream,
+        cuda_event,
+        /*flags (must be zero)=*/0));
+    const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+    if (C10_UNLIKELY(interp)) {
+      (*interp)->trace_gpu_event_wait(
+          c10::kCUDA,
+          reinterpret_cast<uintptr_t>(cuda_event),
+          reinterpret_cast<uintptr_t>(cuda_stream.stream()));
+    }
+    setDevice(orig_device);
+  }
+
+  // May be called from any device
+  bool queryEvent(void* event) const override {
+    if (!event)
+      return true;
+    cudaEvent_t cuda_event = static_cast<cudaEvent_t>(event);
+    // Note: cudaEventQuery can be safely called from any device
+    const cudaError_t err = C10_CUDA_ERROR_HANDLED(cudaEventQuery(cuda_event));
+    if (err != cudaErrorNotReady) {
+      C10_CUDA_CHECK(err);
+    } else {
+      // ignore and clear the error if not ready
+      (void)cudaGetLastError();
+    }
+    return (err == cudaSuccess);
+  }
+
+  // Stream-related functions
+  bool queryStream(const Stream& stream) const override {
+    CUDAStream cuda_stream{stream};
+    return cuda_stream.query();
+  }
+
+  void synchronizeStream(const Stream& stream) const override {
+    CUDAStream cuda_stream{stream};
+    cuda_stream.synchronize();
+  }
+
+  void synchronizeEvent(void* event) const override {
+    if (!event)
+      return;
+    cudaEvent_t cuda_event = static_cast<cudaEvent_t>(event);
+    const c10::impl::PyInterpreter* interp = c10::impl::GPUTrace::get_trace();
+    if (C10_UNLIKELY(interp)) {
+      (*interp)->trace_gpu_event_synchronization(
+          c10::kCUDA, reinterpret_cast<uintptr_t>(cuda_event));
+    }
+    // Note: cudaEventSynchronize can be safely called from any device
+    C10_CUDA_CHECK(cudaEventSynchronize(cuda_event));
+  }
+
+  void recordDataPtrOnStream(const c10::DataPtr& data_ptr, const Stream& stream)
+      const override {
+    CUDAStream cuda_stream{stream};
+    CUDACachingAllocator::recordStream(data_ptr, cuda_stream);
+  }
+
+  double elapsedTime(void* event1, void* event2, const DeviceIndex device_index)
+      const override {
+    TORCH_CHECK(
+        event1 && event2,
+        "Both events must be recorded before calculating elapsed time.");
+    // Even though cudaEventElapsedTime can be safely called from any device, if
+    // the current device is not initialized, it will create a new cuda context,
+    // which will consume a lot of memory.
+    DeviceIndex orig_device{-1};
+    C10_CUDA_CHECK(c10::cuda::GetDevice(&orig_device));
+    C10_CUDA_CHECK(c10::cuda::SetDevice(device_index));
+    cudaEvent_t cuda_event1 = static_cast<cudaEvent_t>(event1);
+    cudaEvent_t cuda_event2 = static_cast<cudaEvent_t>(event2);
+    float time_ms = 0;
+    // raise cudaErrorNotReady if either event is recorded but not yet completed
+    C10_CUDA_CHECK(cudaEventElapsedTime(&time_ms, cuda_event1, cuda_event2));
+    C10_CUDA_CHECK(c10::cuda::SetDevice(orig_device));
+    return static_cast<double>(time_ms);
+  }
+};
+
+} // namespace c10::cuda::impl

videochat2/lib/python3.10/site-packages/torch/include/c10/cuda/impl/CUDATest.h

@@ -0,0 +1,9 @@
+#pragma once
+
+#include <c10/cuda/CUDAMacros.h>
+
+namespace c10::cuda::impl {
+
+C10_CUDA_API int c10_cuda_test();
+
+}

videochat2/lib/python3.10/site-packages/torch/include/c10/util/AlignOf.h

@@ -0,0 +1,176 @@
+//===--- AlignOf.h - Portable calculation of type alignment -----*- C++ -*-===//
+//
+//                     The LLVM Compiler Infrastructure
+//
+// This file is distributed under the University of Illinois Open Source
+// License. See LICENSE.TXT for details.
+//
+//===----------------------------------------------------------------------===//
+//
+// This file defines the AlignedCharArray and AlignedCharArrayUnion classes.
+//
+//===----------------------------------------------------------------------===//
+
+// ATen: modified from llvm::AlignOf
+// replaced LLVM_ALIGNAS with alignas
+
+#pragma once
+
+#include <cstddef>
+
+namespace c10 {
+
+/// \struct AlignedCharArray
+/// \brief Helper for building an aligned character array type.
+///
+/// This template is used to explicitly build up a collection of aligned
+/// character array types. We have to build these up using a macro and explicit
+/// specialization to cope with MSVC (at least till 2015) where only an
+/// integer literal can be used to specify an alignment constraint. Once built
+/// up here, we can then begin to indirect between these using normal C++
+/// template parameters.
+
+// MSVC requires special handling here.
+#ifndef _MSC_VER
+
+template <size_t Alignment, size_t Size>
+struct AlignedCharArray {
+  // NOLINTNEXTLINE(*c-arrays)
+  alignas(Alignment) char buffer[Size];
+};
+
+#else // _MSC_VER
+
+/// \brief Create a type with an aligned char buffer.
+template <size_t Alignment, size_t Size>
+struct AlignedCharArray;
+
+// We provide special variations of this template for the most common
+// alignments because __declspec(align(...)) doesn't actually work when it is
+// a member of a by-value function argument in MSVC, even if the alignment
+// request is something reasonably like 8-byte or 16-byte. Note that we can't
+// even include the declspec with the union that forces the alignment because
+// MSVC warns on the existence of the declspec despite the union member forcing
+// proper alignment.
+
+template <size_t Size>
+struct AlignedCharArray<1, Size> {
+  union {
+    char aligned;
+    char buffer[Size];
+  };
+};
+
+template <size_t Size>
+struct AlignedCharArray<2, Size> {
+  union {
+    short aligned;
+    char buffer[Size];
+  };
+};
+
+template <size_t Size>
+struct AlignedCharArray<4, Size> {
+  union {
+    int aligned;
+    char buffer[Size];
+  };
+};
+
+template <size_t Size>
+struct AlignedCharArray<8, Size> {
+  union {
+    double aligned;
+    char buffer[Size];
+  };
+};
+
+// The rest of these are provided with a __declspec(align(...)) and we simply
+// can't pass them by-value as function arguments on MSVC.
+
+#define AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT(x) \
+  template <size_t Size>                          \
+  struct AlignedCharArray<x, Size> {              \
+    __declspec(align(x)) char buffer[Size];       \
+  };
+
+AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT(16)
+AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT(32)
+AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT(64)
+AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT(128)
+
+#undef AT_ALIGNEDCHARARRAY_TEMPLATE_ALIGNMENT
+
+#endif // _MSC_VER
+
+namespace detail {
+template <
+    typename T1,
+    typename T2 = char,
+    typename T3 = char,
+    typename T4 = char,
+    typename T5 = char,
+    typename T6 = char,
+    typename T7 = char,
+    typename T8 = char,
+    typename T9 = char,
+    typename T10 = char>
+class AlignerImpl {
+  T1 t1;
+  T2 t2;
+  T3 t3;
+  T4 t4;
+  T5 t5;
+  T6 t6;
+  T7 t7;
+  T8 t8;
+  T9 t9;
+  T10 t10;
+
+ public:
+  AlignerImpl() = delete;
+};
+
+template <
+    typename T1,
+    typename T2 = char,
+    typename T3 = char,
+    typename T4 = char,
+    typename T5 = char,
+    typename T6 = char,
+    typename T7 = char,
+    typename T8 = char,
+    typename T9 = char,
+    typename T10 = char>
+union SizerImpl {
+  // NOLINTNEXTLINE(*c-arrays)
+  char arr1[sizeof(T1)], arr2[sizeof(T2)], arr3[sizeof(T3)], arr4[sizeof(T4)],
+      arr5[sizeof(T5)], arr6[sizeof(T6)], arr7[sizeof(T7)], arr8[sizeof(T8)],
+      arr9[sizeof(T9)], arr10[sizeof(T10)];
+};
+} // end namespace detail
+
+/// \brief This union template exposes a suitably aligned and sized character
+/// array member which can hold elements of any of up to ten types.
+///
+/// These types may be arrays, structs, or any other types. The goal is to
+/// expose a char array buffer member which can be used as suitable storage for
+/// a placement new of any of these types. Support for more than ten types can
+/// be added at the cost of more boilerplate.
+template <
+    typename T1,
+    typename T2 = char,
+    typename T3 = char,
+    typename T4 = char,
+    typename T5 = char,
+    typename T6 = char,
+    typename T7 = char,
+    typename T8 = char,
+    typename T9 = char,
+    typename T10 = char>
+struct AlignedCharArrayUnion
+    : AlignedCharArray<
+          alignof(detail::AlignerImpl<T1, T2, T3, T4, T5, T6, T7, T8, T9, T10>),
+          sizeof(::c10::detail::
+                     SizerImpl<T1, T2, T3, T4, T5, T6, T7, T8, T9, T10>)> {};
+} // end namespace c10

videochat2/lib/python3.10/site-packages/torch/include/c10/util/Array.h

@@ -0,0 +1,18 @@
+#pragma once
+
+#include <array>
+#include <utility>
+
+namespace c10 {
+
+// This helper function creates a constexpr std::array
+// From a compile time list of values, without requiring you to explicitly
+// write out the length.
+//
+// See also https://stackoverflow.com/a/26351760/23845
+template <typename V, typename... T>
+inline constexpr auto array_of(T&&... t) -> std::array<V, sizeof...(T)> {
+  return {{std::forward<T>(t)...}};
+}
+
+} // namespace c10

videochat2/lib/python3.10/site-packages/torch/include/c10/util/ArrayRef.h

@@ -0,0 +1,380 @@
+//===--- ArrayRef.h - Array Reference Wrapper -------------------*- C++ -*-===//
+//
+//                     The LLVM Compiler Infrastructure
+//
+// This file is distributed under the University of Illinois Open Source
+// License. See LICENSE.TXT for details.
+//
+//===----------------------------------------------------------------------===//
+
+// ATen: modified from llvm::ArrayRef.
+// removed llvm-specific functionality
+// removed some implicit const -> non-const conversions that rely on
+// complicated std::enable_if meta-programming
+// removed a bunch of slice variants for simplicity...
+
+#pragma once
+
+#include <c10/macros/Macros.h>
+#include <c10/util/Deprecated.h>
+#include <c10/util/Exception.h>
+#include <c10/util/SmallVector.h>
+
+#include <array>
+#include <cstddef>
+#include <cstdint>
+#include <initializer_list>
+#include <iterator>
+#include <ostream>
+#include <type_traits>
+#include <vector>
+
+namespace c10 {
+/// ArrayRef - Represent a constant reference to an array (0 or more elements
+/// consecutively in memory), i.e. a start pointer and a length.  It allows
+/// various APIs to take consecutive elements easily and conveniently.
+///
+/// This class does not own the underlying data, it is expected to be used in
+/// situations where the data resides in some other buffer, whose lifetime
+/// extends past that of the ArrayRef. For this reason, it is not in general
+/// safe to store an ArrayRef.
+///
+/// This is intended to be trivially copyable, so it should be passed by
+/// value.
+template <typename T>
+class ArrayRef final {
+ public:
+  using iterator = const T*;
+  using const_iterator = const T*;
+  using size_type = size_t;
+  using value_type = T;
+
+  using reverse_iterator = std::reverse_iterator<iterator>;
+
+ private:
+  /// The start of the array, in an external buffer.
+  const T* Data;
+
+  /// The number of elements.
+  size_type Length;
+
+  void debugCheckNullptrInvariant() {
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+        Data != nullptr || Length == 0,
+        "created ArrayRef with nullptr and non-zero length! std::optional relies on this being illegal");
+  }
+
+ public:
+  /// @name Constructors
+  /// @{
+
+  /// Construct an empty ArrayRef.
+  /* implicit */ constexpr ArrayRef() : Data(nullptr), Length(0) {}
+
+  /// Construct an ArrayRef from a single element.
+  // TODO Make this explicit
+  constexpr ArrayRef(const T& OneElt) : Data(&OneElt), Length(1) {}
+
+  /// Construct an ArrayRef from a pointer and length.
+  C10_HOST_CONSTEXPR_EXCEPT_WIN_CUDA ArrayRef(const T* data, size_t length)
+      : Data(data), Length(length) {
+    debugCheckNullptrInvariant();
+  }
+
+  /// Construct an ArrayRef from a range.
+  C10_HOST_CONSTEXPR_EXCEPT_WIN_CUDA ArrayRef(const T* begin, const T* end)
+      : Data(begin), Length(end - begin) {
+    debugCheckNullptrInvariant();
+  }
+
+  /// Construct an ArrayRef from a SmallVector. This is templated in order to
+  /// avoid instantiating SmallVectorTemplateCommon<T> whenever we
+  /// copy-construct an ArrayRef.
+  template <typename U>
+  /* implicit */ ArrayRef(const SmallVectorTemplateCommon<T, U>& Vec)
+      : Data(Vec.data()), Length(Vec.size()) {
+    debugCheckNullptrInvariant();
+  }
+
+  template <
+      typename Container,
+      typename = std::enable_if_t<std::is_same_v<
+          std::remove_const_t<decltype(std::declval<Container>().data())>,
+          T*>>>
+  /* implicit */ ArrayRef(const Container& container)
+      : Data(container.data()), Length(container.size()) {
+    debugCheckNullptrInvariant();
+  }
+
+  /// Construct an ArrayRef from a std::vector.
+  // The enable_if stuff here makes sure that this isn't used for
+  // std::vector<bool>, because ArrayRef can't work on a std::vector<bool>
+  // bitfield.
+  template <typename A>
+  /* implicit */ ArrayRef(const std::vector<T, A>& Vec)
+      : Data(Vec.data()), Length(Vec.size()) {
+    static_assert(
+        !std::is_same<T, bool>::value,
+        "ArrayRef<bool> cannot be constructed from a std::vector<bool> bitfield.");
+  }
+
+  /// Construct an ArrayRef from a std::array
+  template <size_t N>
+  /* implicit */ constexpr ArrayRef(const std::array<T, N>& Arr)
+      : Data(Arr.data()), Length(N) {}
+
+  /// Construct an ArrayRef from a C array.
+  template <size_t N>
+  // NOLINTNEXTLINE(*c-arrays*)
+  /* implicit */ constexpr ArrayRef(const T (&Arr)[N]) : Data(Arr), Length(N) {}
+
+  /// Construct an ArrayRef from a std::initializer_list.
+  /* implicit */ constexpr ArrayRef(const std::initializer_list<T>& Vec)
+      : Data(
+            std::begin(Vec) == std::end(Vec) ? static_cast<T*>(nullptr)
+                                             : std::begin(Vec)),
+        Length(Vec.size()) {}
+
+  /// @}
+  /// @name Simple Operations
+  /// @{
+
+  constexpr iterator begin() const {
+    return Data;
+  }
+  constexpr iterator end() const {
+    return Data + Length;
+  }
+
+  // These are actually the same as iterator, since ArrayRef only
+  // gives you const iterators.
+  constexpr const_iterator cbegin() const {
+    return Data;
+  }
+  constexpr const_iterator cend() const {
+    return Data + Length;
+  }
+
+  constexpr reverse_iterator rbegin() const {
+    return reverse_iterator(end());
+  }
+  constexpr reverse_iterator rend() const {
+    return reverse_iterator(begin());
+  }
+
+  /// empty - Check if the array is empty.
+  constexpr bool empty() const {
+    return Length == 0;
+  }
+
+  constexpr const T* data() const {
+    return Data;
+  }
+
+  /// size - Get the array size.
+  constexpr size_t size() const {
+    return Length;
+  }
+
+  /// front - Get the first element.
+  C10_HOST_CONSTEXPR_EXCEPT_WIN_CUDA const T& front() const {
+    TORCH_CHECK(
+        !empty(), "ArrayRef: attempted to access front() of empty list");
+    return Data[0];
+  }
+
+  /// back - Get the last element.
+  C10_HOST_CONSTEXPR_EXCEPT_WIN_CUDA const T& back() const {
+    TORCH_CHECK(!empty(), "ArrayRef: attempted to access back() of empty list");
+    return Data[Length - 1];
+  }
+
+  /// equals - Check for element-wise equality.
+  constexpr bool equals(ArrayRef RHS) const {
+    return Length == RHS.Length && std::equal(begin(), end(), RHS.begin());
+  }
+
+  /// slice(n, m) - Take M elements of the array starting at element N
+  C10_HOST_CONSTEXPR_EXCEPT_WIN_CUDA ArrayRef<T> slice(size_t N, size_t M)
+      const {
+    TORCH_CHECK(
+        N + M <= size(),
+        "ArrayRef: invalid slice, N = ",
+        N,
+        "; M = ",
+        M,
+        "; size = ",
+        size());
+    return ArrayRef<T>(data() + N, M);
+  }
+
+  /// slice(n) - Chop off the first N elements of the array.
+  C10_HOST_CONSTEXPR_EXCEPT_WIN_CUDA ArrayRef<T> slice(size_t N) const {
+    TORCH_CHECK(
+        N <= size(), "ArrayRef: invalid slice, N = ", N, "; size = ", size());
+    return slice(N, size() - N);
+  }
+
+  /// @}
+  /// @name Operator Overloads
+  /// @{
+  constexpr const T& operator[](size_t Index) const {
+    return Data[Index];
+  }
+
+  /// Vector compatibility
+  C10_HOST_CONSTEXPR_EXCEPT_WIN_CUDA const T& at(size_t Index) const {
+    TORCH_CHECK(
+        Index < Length,
+        "ArrayRef: invalid index Index = ",
+        Index,
+        "; Length = ",
+        Length);
+    return Data[Index];
+  }
+
+  /// Disallow accidental assignment from a temporary.
+  ///
+  /// The declaration here is extra complicated so that "arrayRef = {}"
+  /// continues to select the move assignment operator.
+  template <typename U>
+  std::enable_if_t<std::is_same_v<U, T>, ArrayRef<T>>& operator=(
+      // NOLINTNEXTLINE(cppcoreguidelines-missing-std-forward)
+      U&& Temporary) = delete;
+
+  /// Disallow accidental assignment from a temporary.
+  ///
+  /// The declaration here is extra complicated so that "arrayRef = {}"
+  /// continues to select the move assignment operator.
+  template <typename U>
+  std::enable_if_t<std::is_same_v<U, T>, ArrayRef<T>>& operator=(
+      std::initializer_list<U>) = delete;
+
+  /// @}
+  /// @name Expensive Operations
+  /// @{
+  std::vector<T> vec() const {
+    return std::vector<T>(Data, Data + Length);
+  }
+
+  /// @}
+};
+
+template <typename T>
+std::ostream& operator<<(std::ostream& out, ArrayRef<T> list) {
+  int i = 0;
+  out << "[";
+  for (const auto& e : list) {
+    if (i++ > 0)
+      out << ", ";
+    out << e;
+  }
+  out << "]";
+  return out;
+}
+
+/// @name ArrayRef Convenience constructors
+/// @{
+
+/// Construct an ArrayRef from a single element.
+template <typename T>
+ArrayRef<T> makeArrayRef(const T& OneElt) {
+  return OneElt;
+}
+
+/// Construct an ArrayRef from a pointer and length.
+template <typename T>
+ArrayRef<T> makeArrayRef(const T* data, size_t length) {
+  return ArrayRef<T>(data, length);
+}
+
+/// Construct an ArrayRef from a range.
+template <typename T>
+ArrayRef<T> makeArrayRef(const T* begin, const T* end) {
+  return ArrayRef<T>(begin, end);
+}
+
+/// Construct an ArrayRef from a SmallVector.
+template <typename T>
+ArrayRef<T> makeArrayRef(const SmallVectorImpl<T>& Vec) {
+  return Vec;
+}
+
+/// Construct an ArrayRef from a SmallVector.
+template <typename T, unsigned N>
+ArrayRef<T> makeArrayRef(const SmallVector<T, N>& Vec) {
+  return Vec;
+}
+
+/// Construct an ArrayRef from a std::vector.
+template <typename T>
+ArrayRef<T> makeArrayRef(const std::vector<T>& Vec) {
+  return Vec;
+}
+
+/// Construct an ArrayRef from a std::array.
+template <typename T, std::size_t N>
+ArrayRef<T> makeArrayRef(const std::array<T, N>& Arr) {
+  return Arr;
+}
+
+/// Construct an ArrayRef from an ArrayRef (no-op) (const)
+template <typename T>
+ArrayRef<T> makeArrayRef(const ArrayRef<T>& Vec) {
+  return Vec;
+}
+
+/// Construct an ArrayRef from an ArrayRef (no-op)
+template <typename T>
+ArrayRef<T>& makeArrayRef(ArrayRef<T>& Vec) {
+  return Vec;
+}
+
+/// Construct an ArrayRef from a C array.
+template <typename T, size_t N>
+// NOLINTNEXTLINE(*c-arrays*)
+ArrayRef<T> makeArrayRef(const T (&Arr)[N]) {
+  return ArrayRef<T>(Arr);
+}
+
+// WARNING: Template instantiation will NOT be willing to do an implicit
+// conversions to get you to an c10::ArrayRef, which is why we need so
+// many overloads.
+
+template <typename T>
+bool operator==(c10::ArrayRef<T> a1, c10::ArrayRef<T> a2) {
+  return a1.equals(a2);
+}
+
+template <typename T>
+bool operator!=(c10::ArrayRef<T> a1, c10::ArrayRef<T> a2) {
+  return !a1.equals(a2);
+}
+
+template <typename T>
+bool operator==(const std::vector<T>& a1, c10::ArrayRef<T> a2) {
+  return c10::ArrayRef<T>(a1).equals(a2);
+}
+
+template <typename T>
+bool operator!=(const std::vector<T>& a1, c10::ArrayRef<T> a2) {
+  return !c10::ArrayRef<T>(a1).equals(a2);
+}
+
+template <typename T>
+bool operator==(c10::ArrayRef<T> a1, const std::vector<T>& a2) {
+  return a1.equals(c10::ArrayRef<T>(a2));
+}
+
+template <typename T>
+bool operator!=(c10::ArrayRef<T> a1, const std::vector<T>& a2) {
+  return !a1.equals(c10::ArrayRef<T>(a2));
+}
+
+using IntArrayRef = ArrayRef<int64_t>;
+
+// This alias is deprecated because it doesn't make ownership
+// semantics obvious.  Use IntArrayRef instead!
+C10_DEFINE_DEPRECATED_USING(IntList, ArrayRef<int64_t>)
+
+} // namespace c10

videochat2/lib/python3.10/site-packages/torch/include/c10/util/BFloat16-inl.h

@@ -0,0 +1,361 @@
+#pragma once
+
+#include <c10/macros/Macros.h>
+#include <c10/util/bit_cast.h>
+
+#include <limits>
+
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+#if defined(SYCL_EXT_ONEAPI_BFLOAT16_MATH_FUNCTIONS)
+#if defined(CL_SYCL_LANGUAGE_VERSION)
+#include <CL/sycl.hpp> // for SYCL 1.2.1
+#else
+#include <sycl/sycl.hpp> // for SYCL 2020
+#endif
+#include <ext/oneapi/bfloat16.hpp>
+#endif
+
+namespace c10 {
+
+/// Constructors
+inline C10_HOST_DEVICE BFloat16::BFloat16(float value)
+    :
+#if defined(__CUDACC__) && !defined(USE_ROCM) && defined(__CUDA_ARCH__) && \
+    __CUDA_ARCH__ >= 800
+      x(__bfloat16_as_ushort(__float2bfloat16(value)))
+#elif defined(__SYCL_DEVICE_ONLY__) && \
+    defined(SYCL_EXT_ONEAPI_BFLOAT16_MATH_FUNCTIONS)
+      x(c10::bit_cast<uint16_t>(sycl::ext::oneapi::bfloat16(value)))
+#else
+      // RNE by default
+      x(detail::round_to_nearest_even(value))
+#endif
+{
+}
+
+/// Implicit conversions
+inline C10_HOST_DEVICE BFloat16::operator float() const {
+#if defined(__CUDACC__) && !defined(USE_ROCM)
+  return __bfloat162float(*reinterpret_cast<const __nv_bfloat16*>(&x));
+#elif defined(__SYCL_DEVICE_ONLY__) && \
+    defined(SYCL_EXT_ONEAPI_BFLOAT16_MATH_FUNCTIONS)
+  return float(*reinterpret_cast<const sycl::ext::oneapi::bfloat16*>(&x));
+#else
+  return detail::f32_from_bits(x);
+#endif
+}
+
+#if defined(__CUDACC__) && !defined(USE_ROCM)
+inline C10_HOST_DEVICE BFloat16::BFloat16(const __nv_bfloat16& value) {
+  x = *reinterpret_cast<const unsigned short*>(&value);
+}
+inline C10_HOST_DEVICE BFloat16::operator __nv_bfloat16() const {
+  return *reinterpret_cast<const __nv_bfloat16*>(&x);
+}
+#endif
+#if defined(__HIPCC__) && defined(USE_ROCM)
+// 6.2.0 introduced __hip_bfloat16_raw
+#if defined(__BF16_HOST_DEVICE__)
+inline C10_HOST_DEVICE BFloat16::BFloat16(const __hip_bfloat16& value) {
+  x = __hip_bfloat16_raw(value).x;
+}
+inline C10_HOST_DEVICE BFloat16::operator __hip_bfloat16() const {
+  return __hip_bfloat16(__hip_bfloat16_raw{x});
+}
+#else // !defined(__BF16_HOST_DEVICE__)
+inline C10_HOST_DEVICE BFloat16::BFloat16(const __hip_bfloat16& value) {
+  x = value.data;
+}
+inline C10_HOST_DEVICE BFloat16::operator __hip_bfloat16() const {
+  return __hip_bfloat16{x};
+}
+#endif // !defined(__BF16_HOST_DEVICE__)
+#endif // defined(__HIPCC__) && defined(USE_ROCM)
+
+#if defined(SYCL_EXT_ONEAPI_BFLOAT16_MATH_FUNCTIONS)
+inline C10_HOST_DEVICE BFloat16::BFloat16(
+    const sycl::ext::oneapi::bfloat16& value) {
+  x = *reinterpret_cast<const unsigned short*>(&value);
+}
+inline C10_HOST_DEVICE BFloat16::operator sycl::ext::oneapi::bfloat16() const {
+  return *reinterpret_cast<const sycl::ext::oneapi::bfloat16*>(&x);
+}
+#endif
+
+// CUDA intrinsics
+
+#if defined(__CUDACC__) || defined(__HIPCC__)
+inline C10_DEVICE BFloat16 __ldg(const BFloat16* ptr) {
+#if !defined(USE_ROCM) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
+  return __ldg(reinterpret_cast<const __nv_bfloat16*>(ptr));
+#else
+  return *ptr;
+#endif
+}
+#endif
+
+/// Arithmetic
+
+inline C10_HOST_DEVICE BFloat16
+operator+(const BFloat16& a, const BFloat16& b) {
+  return static_cast<float>(a) + static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16
+operator-(const BFloat16& a, const BFloat16& b) {
+  return static_cast<float>(a) - static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16
+operator*(const BFloat16& a, const BFloat16& b) {
+  return static_cast<float>(a) * static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16 operator/(const BFloat16& a, const BFloat16& b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16 operator-(const BFloat16& a) {
+  return -static_cast<float>(a);
+}
+
+inline C10_HOST_DEVICE BFloat16& operator+=(BFloat16& a, const BFloat16& b) {
+  a = a + b;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator-=(BFloat16& a, const BFloat16& b) {
+  a = a - b;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator*=(BFloat16& a, const BFloat16& b) {
+  a = a * b;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator/=(BFloat16& a, const BFloat16& b) {
+  a = a / b;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator|(BFloat16& a, const BFloat16& b) {
+  a.x = a.x | b.x;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator^(BFloat16& a, const BFloat16& b) {
+  a.x = a.x ^ b.x;
+  return a;
+}
+
+inline C10_HOST_DEVICE BFloat16& operator&(BFloat16& a, const BFloat16& b) {
+  a.x = a.x & b.x;
+  return a;
+}
+
+/// Arithmetic with floats
+
+inline C10_HOST_DEVICE float operator+(BFloat16 a, float b) {
+  return static_cast<float>(a) + b;
+}
+inline C10_HOST_DEVICE float operator-(BFloat16 a, float b) {
+  return static_cast<float>(a) - b;
+}
+inline C10_HOST_DEVICE float operator*(BFloat16 a, float b) {
+  return static_cast<float>(a) * b;
+}
+inline C10_HOST_DEVICE float operator/(BFloat16 a, float b) {
+  return static_cast<float>(a) / b;
+}
+
+inline C10_HOST_DEVICE float operator+(float a, BFloat16 b) {
+  return a + static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator-(float a, BFloat16 b) {
+  return a - static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator*(float a, BFloat16 b) {
+  return a * static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator/(float a, BFloat16 b) {
+  return a / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE float& operator+=(float& a, const BFloat16& b) {
+  return a += static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator-=(float& a, const BFloat16& b) {
+  return a -= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator*=(float& a, const BFloat16& b) {
+  return a *= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator/=(float& a, const BFloat16& b) {
+  return a /= static_cast<float>(b);
+}
+
+/// Arithmetic with doubles
+
+inline C10_HOST_DEVICE double operator+(BFloat16 a, double b) {
+  return static_cast<double>(a) + b;
+}
+inline C10_HOST_DEVICE double operator-(BFloat16 a, double b) {
+  return static_cast<double>(a) - b;
+}
+inline C10_HOST_DEVICE double operator*(BFloat16 a, double b) {
+  return static_cast<double>(a) * b;
+}
+inline C10_HOST_DEVICE double operator/(BFloat16 a, double b) {
+  return static_cast<double>(a) / b;
+}
+
+inline C10_HOST_DEVICE double operator+(double a, BFloat16 b) {
+  return a + static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator-(double a, BFloat16 b) {
+  return a - static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator*(double a, BFloat16 b) {
+  return a * static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator/(double a, BFloat16 b) {
+  return a / static_cast<double>(b);
+}
+
+/// Arithmetic with ints
+
+inline C10_HOST_DEVICE BFloat16 operator+(BFloat16 a, int b) {
+  return a + static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator-(BFloat16 a, int b) {
+  return a - static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator*(BFloat16 a, int b) {
+  return a * static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator/(BFloat16 a, int b) {
+  return a / static_cast<BFloat16>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16 operator+(int a, BFloat16 b) {
+  return static_cast<BFloat16>(a) + b;
+}
+inline C10_HOST_DEVICE BFloat16 operator-(int a, BFloat16 b) {
+  return static_cast<BFloat16>(a) - b;
+}
+inline C10_HOST_DEVICE BFloat16 operator*(int a, BFloat16 b) {
+  return static_cast<BFloat16>(a) * b;
+}
+inline C10_HOST_DEVICE BFloat16 operator/(int a, BFloat16 b) {
+  return static_cast<BFloat16>(a) / b;
+}
+
+//// Arithmetic with int64_t
+
+inline C10_HOST_DEVICE BFloat16 operator+(BFloat16 a, int64_t b) {
+  return a + static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator-(BFloat16 a, int64_t b) {
+  return a - static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator*(BFloat16 a, int64_t b) {
+  return a * static_cast<BFloat16>(b);
+}
+inline C10_HOST_DEVICE BFloat16 operator/(BFloat16 a, int64_t b) {
+  return a / static_cast<BFloat16>(b);
+}
+
+inline C10_HOST_DEVICE BFloat16 operator+(int64_t a, BFloat16 b) {
+  return static_cast<BFloat16>(a) + b;
+}
+inline C10_HOST_DEVICE BFloat16 operator-(int64_t a, BFloat16 b) {
+  return static_cast<BFloat16>(a) - b;
+}
+inline C10_HOST_DEVICE BFloat16 operator*(int64_t a, BFloat16 b) {
+  return static_cast<BFloat16>(a) * b;
+}
+inline C10_HOST_DEVICE BFloat16 operator/(int64_t a, BFloat16 b) {
+  return static_cast<BFloat16>(a) / b;
+}
+
+// Overloading < and > operators, because std::max and std::min use them.
+
+inline C10_HOST_DEVICE bool operator>(BFloat16& lhs, BFloat16& rhs) {
+  return float(lhs) > float(rhs);
+}
+
+inline C10_HOST_DEVICE bool operator<(BFloat16& lhs, BFloat16& rhs) {
+  return float(lhs) < float(rhs);
+}
+
+} // namespace c10
+
+namespace std {
+
+template <>
+class numeric_limits<c10::BFloat16> {
+ public:
+  static constexpr bool is_signed = true;
+  static constexpr bool is_specialized = true;
+  static constexpr bool is_integer = false;
+  static constexpr bool is_exact = false;
+  static constexpr bool has_infinity = true;
+  static constexpr bool has_quiet_NaN = true;
+  static constexpr bool has_signaling_NaN = true;
+  static constexpr auto has_denorm = numeric_limits<float>::has_denorm;
+  static constexpr auto has_denorm_loss =
+      numeric_limits<float>::has_denorm_loss;
+  static constexpr auto round_style = numeric_limits<float>::round_style;
+  static constexpr bool is_iec559 = false;
+  static constexpr bool is_bounded = true;
+  static constexpr bool is_modulo = false;
+  static constexpr int digits = 8;
+  static constexpr int digits10 = 2;
+  static constexpr int max_digits10 = 4;
+  static constexpr int radix = 2;
+  static constexpr int min_exponent = -125;
+  static constexpr int min_exponent10 = -37;
+  static constexpr int max_exponent = 128;
+  static constexpr int max_exponent10 = 38;
+  static constexpr auto traps = numeric_limits<float>::traps;
+  static constexpr auto tinyness_before =
+      numeric_limits<float>::tinyness_before;
+
+  static constexpr c10::BFloat16 min() {
+    return c10::BFloat16(0x0080, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 lowest() {
+    return c10::BFloat16(0xFF7F, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 max() {
+    return c10::BFloat16(0x7F7F, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 epsilon() {
+    return c10::BFloat16(0x3C00, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 round_error() {
+    return c10::BFloat16(0x3F00, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 infinity() {
+    return c10::BFloat16(0x7F80, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 quiet_NaN() {
+    return c10::BFloat16(0x7FC0, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 signaling_NaN() {
+    return c10::BFloat16(0x7F80, c10::BFloat16::from_bits());
+  }
+  static constexpr c10::BFloat16 denorm_min() {
+    return c10::BFloat16(0x0001, c10::BFloat16::from_bits());
+  }
+};
+
+} // namespace std
+
+C10_CLANG_DIAGNOSTIC_POP()

videochat2/lib/python3.10/site-packages/torch/include/c10/util/BFloat16-math.h

@@ -0,0 +1,292 @@
+#pragma once
+
+#include <c10/util/BFloat16.h>
+#include <c10/util/Half.h>
+
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-float-conversion")
+#endif
+
+namespace std {
+
+template <typename T>
+struct is_reduced_floating_point
+    : std::integral_constant<
+          bool,
+          std::is_same_v<T, c10::Half> || std::is_same_v<T, c10::BFloat16>> {};
+
+template <typename T>
+constexpr bool is_reduced_floating_point_v =
+    is_reduced_floating_point<T>::value;
+
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T acos(T a) {
+  return std::acos(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T asin(T a) {
+  return std::asin(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T atan(T a) {
+  return std::atan(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T atanh(T a) {
+  return std::atanh(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T erf(T a) {
+  return std::erf(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T erfc(T a) {
+  return std::erfc(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T exp(T a) {
+  return std::exp(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T expm1(T a) {
+  return std::expm1(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline bool isfinite(T a) {
+  return std::isfinite(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T log(T a) {
+  return std::log(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T log10(T a) {
+  return std::log10(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T log1p(T a) {
+  return std::log1p(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T log2(T a) {
+  return std::log2(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T ceil(T a) {
+  return std::ceil(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T cos(T a) {
+  return std::cos(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T floor(T a) {
+  return std::floor(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T nearbyint(T a) {
+  return std::nearbyint(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T sin(T a) {
+  return std::sin(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T tan(T a) {
+  return std::tan(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T sinh(T a) {
+  return std::sinh(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T cosh(T a) {
+  return std::cosh(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T tanh(T a) {
+  return std::tanh(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T trunc(T a) {
+  return std::trunc(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T lgamma(T a) {
+  return std::lgamma(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T sqrt(T a) {
+  return std::sqrt(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T rsqrt(T a) {
+  return 1.0 / std::sqrt(float(a));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T abs(T a) {
+  return std::abs(float(a));
+}
+#if defined(_MSC_VER) && defined(__CUDACC__)
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T pow(T a, double b) {
+  return std::pow(float(a), float(b));
+}
+#else
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T pow(T a, double b) {
+  return std::pow(float(a), b);
+}
+#endif
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T pow(T a, T b) {
+  return std::pow(float(a), float(b));
+}
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+inline T fmod(T a, T b) {
+  return std::fmod(float(a), float(b));
+}
+
+/*
+  The following function is inspired from the implementation in `musl`
+  Link to License: https://git.musl-libc.org/cgit/musl/tree/COPYRIGHT
+  ----------------------------------------------------------------------
+  Copyright © 2005-2020 Rich Felker, et al.
+
+  Permission is hereby granted, free of charge, to any person obtaining
+  a copy of this software and associated documentation files (the
+  "Software"), to deal in the Software without restriction, including
+  without limitation the rights to use, copy, modify, merge, publish,
+  distribute, sublicense, and/or sell copies of the Software, and to
+  permit persons to whom the Software is furnished to do so, subject to
+  the following conditions:
+
+  The above copyright notice and this permission notice shall be
+  included in all copies or substantial portions of the Software.
+
+  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+  EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+  MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+  IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+  CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+  TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+  SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+  ----------------------------------------------------------------------
+ */
+template <
+    typename T,
+    typename std::enable_if_t<is_reduced_floating_point_v<T>, int> = 0>
+C10_HOST_DEVICE inline T nextafter(T from, T to) {
+  // Reference:
+  // https://git.musl-libc.org/cgit/musl/tree/src/math/nextafter.c
+  using int_repr_t = uint16_t;
+  constexpr uint8_t bits = 16;
+  union {
+    T f;
+    int_repr_t i;
+  } ufrom = {from}, uto = {to};
+
+  // get a mask to get the sign bit i.e. MSB
+  int_repr_t sign_mask = int_repr_t{1} << (bits - 1);
+
+  // short-circuit: if either is NaN, return NaN
+  if (from != from || to != to) {
+    return from + to;
+  }
+
+  // short-circuit: if they are exactly the same.
+  if (ufrom.i == uto.i) {
+    return from;
+  }
+
+  // mask the sign-bit to zero i.e. positive
+  // equivalent to abs(x)
+  int_repr_t abs_from = ufrom.i & ~sign_mask;
+  int_repr_t abs_to = uto.i & ~sign_mask;
+  if (abs_from == 0) {
+    // if both are zero but with different sign,
+    // preserve the sign of `to`.
+    if (abs_to == 0) {
+      return to;
+    }
+    // smallest subnormal with sign of `to`.
+    ufrom.i = (uto.i & sign_mask) | int_repr_t{1};
+    return ufrom.f;
+  }
+
+  // if abs(from) > abs(to) or sign(from) != sign(to)
+  if (abs_from > abs_to || ((ufrom.i ^ uto.i) & sign_mask)) {
+    ufrom.i--;
+  } else {
+    ufrom.i++;
+  }
+
+  return ufrom.f;
+}
+
+} // namespace std
+
+C10_CLANG_DIAGNOSTIC_POP()

videochat2/lib/python3.10/site-packages/torch/include/c10/util/Backtrace.h

@@ -0,0 +1,31 @@
+#ifndef C10_UTIL_BACKTRACE_H_
+#define C10_UTIL_BACKTRACE_H_
+
+#include <cstddef>
+#include <memory>
+#include <string>
+#include <typeinfo>
+
+#include <c10/macros/Macros.h>
+#include <c10/util/Lazy.h>
+
+namespace c10 {
+
+// Symbolizing the backtrace can be expensive; pass it around as a lazy string
+// so it is symbolized only if actually needed.
+using Backtrace = std::shared_ptr<const LazyValue<std::string>>;
+
+// DEPRECATED: Prefer get_lazy_backtrace().
+C10_API std::string get_backtrace(
+    size_t frames_to_skip = 0,
+    size_t maximum_number_of_frames = 64,
+    bool skip_python_frames = true);
+
+C10_API Backtrace get_lazy_backtrace(
+    size_t frames_to_skip = 0,
+    size_t maximum_number_of_frames = 64,
+    bool skip_python_frames = true);
+
+} // namespace c10
+
+#endif // C10_UTIL_BACKTRACE_H_

videochat2/lib/python3.10/site-packages/torch/include/c10/util/C++17.h

@@ -0,0 +1,142 @@
+#pragma once
+#ifndef C10_UTIL_CPP17_H_
+#define C10_UTIL_CPP17_H_
+
+#include <c10/macros/Macros.h>
+#include <functional>
+#include <memory>
+#include <type_traits>
+#include <utility>
+
+#if !defined(__clang__) && !defined(_MSC_VER) && defined(__GNUC__) && \
+    __GNUC__ < 9
+#error \
+    "You're trying to build PyTorch with a too old version of GCC. We need GCC 9 or later."
+#endif
+
+#if defined(__clang__) && __clang_major__ < 9
+#error \
+    "You're trying to build PyTorch with a too old version of Clang. We need Clang 9 or later."
+#endif
+
+#if (defined(_MSC_VER) && (!defined(_MSVC_LANG) || _MSVC_LANG < 201703L)) || \
+    (!defined(_MSC_VER) && __cplusplus < 201703L)
+#error You need C++17 to compile PyTorch
+#endif
+
+#if defined(_WIN32) && (defined(min) || defined(max))
+#error Macro clash with min and max -- define NOMINMAX when compiling your program on Windows
+#endif
+
+/*
+ * This header adds some polyfills with C++17 functionality
+ */
+
+namespace c10 {
+
+// std::is_pod is deprecated in C++20, std::is_standard_layout and
+// std::is_trivial are introduced in C++11, std::conjunction has been introduced
+// in C++17.
+template <typename T>
+using is_pod = std::conjunction<std::is_standard_layout<T>, std::is_trivial<T>>;
+
+template <typename T>
+constexpr bool is_pod_v = is_pod<T>::value;
+
+namespace guts {
+
+template <typename Base, typename Child, typename... Args>
+std::enable_if_t<
+    !std::is_array_v<Base> && !std::is_array_v<Child> &&
+        std::is_base_of_v<Base, Child>,
+    std::unique_ptr<Base>>
+make_unique_base(Args&&... args) {
+  return std::unique_ptr<Base>(new Child(std::forward<Args>(args)...));
+}
+
+#if defined(__cpp_lib_apply) && !defined(__CUDA_ARCH__) && !defined(__HIP__)
+
+template <class F, class Tuple>
+C10_HOST_DEVICE inline constexpr decltype(auto) apply(F&& f, Tuple&& t) {
+  return std::apply(std::forward<F>(f), std::forward<Tuple>(t));
+}
+
+#else
+
+// Implementation from http://en.cppreference.com/w/cpp/utility/apply (but
+// modified)
+// TODO This is an incomplete implementation of std::apply, not working for
+// member functions.
+namespace detail {
+template <class F, class Tuple, std::size_t... INDEX>
+#if defined(_MSC_VER)
+// MSVC has a problem with the decltype() return type, but it also doesn't need
+// it
+C10_HOST_DEVICE constexpr auto apply_impl(
+    F&& f,
+    Tuple&& t,
+    std::index_sequence<INDEX...>)
+#else
+// GCC/Clang need the decltype() return type
+C10_HOST_DEVICE constexpr decltype(auto) apply_impl(
+    F&& f,
+    Tuple&& t,
+    std::index_sequence<INDEX...>)
+#endif
+{
+  return std::forward<F>(f)(std::get<INDEX>(std::forward<Tuple>(t))...);
+}
+} // namespace detail
+
+template <class F, class Tuple>
+C10_HOST_DEVICE constexpr decltype(auto) apply(F&& f, Tuple&& t) {
+  return detail::apply_impl(
+      std::forward<F>(f),
+      std::forward<Tuple>(t),
+      std::make_index_sequence<
+          std::tuple_size<std::remove_reference_t<Tuple>>::value>{});
+}
+
+#endif
+
+template <typename Functor, typename... Args>
+std::enable_if_t<
+    std::is_member_pointer_v<std::decay_t<Functor>>,
+    typename std::invoke_result_t<Functor, Args...>>
+invoke(Functor&& f, Args&&... args) {
+  return std::mem_fn(std::forward<Functor>(f))(std::forward<Args>(args)...);
+}
+
+template <typename Functor, typename... Args>
+std::enable_if_t<
+    !std::is_member_pointer_v<std::decay_t<Functor>>,
+    typename std::invoke_result_t<Functor, Args...>>
+invoke(Functor&& f, Args&&... args) {
+  return std::forward<Functor>(f)(std::forward<Args>(args)...);
+}
+
+namespace detail {
+struct _identity final {
+  template <class T>
+  using type_identity = T;
+
+  template <class T>
+  decltype(auto) operator()(T&& arg) {
+    return std::forward<T>(arg);
+  }
+};
+
+template <class Func, class Enable = void>
+struct function_takes_identity_argument : std::false_type {};
+
+template <class Func>
+struct function_takes_identity_argument<
+    Func,
+    std::void_t<decltype(std::declval<Func>()(_identity()))>> : std::true_type {
+};
+} // namespace detail
+
+} // namespace guts
+} // namespace c10
+
+#endif // C10_UTIL_CPP17_H_

videochat2/lib/python3.10/site-packages/torch/include/c10/util/CallOnce.h

@@ -0,0 +1,67 @@
+#pragma once
+
+#include <atomic>
+#include <mutex>
+#include <utility>
+
+#include <c10/macros/Macros.h>
+#include <c10/util/C++17.h>
+
+namespace c10 {
+
+// custom c10 call_once implementation to avoid the deadlock in std::call_once.
+// The implementation here is a simplified version from folly and likely much
+// much higher memory footprint.
+template <typename Flag, typename F, typename... Args>
+inline void call_once(Flag& flag, F&& f, Args&&... args) {
+  if (C10_LIKELY(flag.test_once())) {
+    return;
+  }
+  flag.call_once_slow(std::forward<F>(f), std::forward<Args>(args)...);
+}
+
+class once_flag {
+ public:
+#ifndef _WIN32
+  // running into build error on MSVC. Can't seem to get a repro locally so I'm
+  // just avoiding constexpr
+  //
+  //   C:/actions-runner/_work/pytorch/pytorch\c10/util/CallOnce.h(26): error:
+  //   defaulted default constructor cannot be constexpr because the
+  //   corresponding implicitly declared default constructor would not be
+  //   constexpr 1 error detected in the compilation of
+  //   "C:/actions-runner/_work/pytorch/pytorch/aten/src/ATen/cuda/cub.cu".
+  constexpr
+#endif
+      once_flag() noexcept = default;
+  once_flag(const once_flag&) = delete;
+  once_flag& operator=(const once_flag&) = delete;
+
+ private:
+  template <typename Flag, typename F, typename... Args>
+  friend void call_once(Flag& flag, F&& f, Args&&... args);
+
+  template <typename F, typename... Args>
+  void call_once_slow(F&& f, Args&&... args) {
+    std::lock_guard<std::mutex> guard(mutex_);
+    if (init_.load(std::memory_order_relaxed)) {
+      return;
+    }
+    c10::guts::invoke(std::forward<F>(f), std::forward<Args>(args)...);
+    init_.store(true, std::memory_order_release);
+  }
+
+  bool test_once() {
+    return init_.load(std::memory_order_acquire);
+  }
+
+  void reset_once() {
+    init_.store(false, std::memory_order_release);
+  }
+
+ private:
+  std::mutex mutex_;
+  std::atomic<bool> init_{false};
+};
+
+} // namespace c10

videochat2/lib/python3.10/site-packages/torch/include/c10/util/DeadlockDetection.h

@@ -0,0 +1,48 @@
+#pragma once
+
+#include <c10/macros/Export.h>
+#include <c10/util/Exception.h>
+
+/// This file provides some simple utilities for detecting common deadlocks in
+/// PyTorch.  For now, we focus exclusively on detecting Python GIL deadlocks,
+/// as the GIL is a wide ranging lock that is taken out in many situations.
+/// The basic strategy is before performing an operation that may block, you
+/// can use TORCH_ASSERT_NO_GIL_WITHOUT_PYTHON_DEP() to assert that the GIL is
+/// not held.  This macro is to be used in contexts where no static dependency
+/// on Python is available (we will handle indirecting a virtual call for you).
+///
+/// If the GIL is held by a torchdeploy interpreter, we always report false.
+/// If you are in a context where Python bindings are available, it's better
+/// to directly assert on PyGILState_Check (as it avoids a vcall and also
+/// works correctly with torchdeploy.)
+
+#define TORCH_ASSERT_NO_GIL_WITHOUT_PYTHON_DEP() \
+  TORCH_INTERNAL_ASSERT(                         \
+      !c10::impl::check_python_gil(),            \
+      "Holding GIL before a blocking operation!  Please release the GIL before blocking, or see https://github.com/pytorch/pytorch/issues/56297 for how to release the GIL for destructors of objects")
+
+namespace c10::impl {
+
+C10_API bool check_python_gil();
+
+struct C10_API PythonGILHooks {
+  virtual ~PythonGILHooks() = default;
+  // Returns true if we hold the GIL.  If not linked against Python we
+  // always return false.
+  virtual bool check_python_gil() const = 0;
+};
+
+C10_API void SetPythonGILHooks(PythonGILHooks* factory);
+
+// DO NOT call this registerer from a torch deploy instance!  You will clobber
+// other registrations
+struct C10_API PythonGILHooksRegisterer {
+  explicit PythonGILHooksRegisterer(PythonGILHooks* factory) {
+    SetPythonGILHooks(factory);
+  }
+  ~PythonGILHooksRegisterer() {
+    SetPythonGILHooks(nullptr);
+  }
+};
+
+} // namespace c10::impl

videochat2/lib/python3.10/site-packages/torch/include/c10/util/Deprecated.h

@@ -0,0 +1,102 @@
+#pragma once
+
+/**
+ * This file provides portable macros for marking declarations
+ * as deprecated.  You should generally use C10_DEPRECATED,
+ * except when marking 'using' declarations as deprecated,
+ * in which case you should use C10_DEFINE_DEPRECATED_USING
+ * (due to portability concerns).
+ */
+
+// Sample usage:
+//
+//    C10_DEPRECATED void bad_func();
+//    struct C10_DEPRECATED BadStruct {
+//      ...
+//    };
+
+// NB: __cplusplus doesn't work for MSVC, so for now MSVC always uses
+// the "__declspec(deprecated)" implementation and not the C++14
+// "[[deprecated]]" attribute. We tried enabling "[[deprecated]]" for C++14 on
+// MSVC, but ran into issues with some older MSVC versions.
+#if (defined(__cplusplus) && __cplusplus >= 201402L)
+#define C10_DEPRECATED [[deprecated]]
+#define C10_DEPRECATED_MESSAGE(message) [[deprecated(message)]]
+#elif defined(__GNUC__)
+#define C10_DEPRECATED __attribute__((deprecated))
+// TODO Is there some way to implement this?
+#define C10_DEPRECATED_MESSAGE(message) __attribute__((deprecated))
+
+#elif defined(_MSC_VER)
+#define C10_DEPRECATED __declspec(deprecated)
+#define C10_DEPRECATED_MESSAGE(message) __declspec(deprecated(message))
+#else
+#warning "You need to implement C10_DEPRECATED for this compiler"
+#define C10_DEPRECATED
+#endif
+
+// Sample usage:
+//
+//    C10_DEFINE_DEPRECATED_USING(BadType, int)
+//
+//   which is the portable version of
+//
+//    using BadType [[deprecated]] = int;
+
+// technically [[deprecated]] syntax is from c++14 standard, but it works in
+// many compilers.
+#if defined(__has_cpp_attribute)
+#if __has_cpp_attribute(deprecated) && !defined(__CUDACC__)
+#define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) \
+  using TypeName [[deprecated]] = TypeThingy;
+#endif
+#endif
+
+#if defined(_MSC_VER)
+#if defined(__CUDACC__)
+// neither [[deprecated]] nor __declspec(deprecated) work on nvcc on Windows;
+// you get the error:
+//
+//    error: attribute does not apply to any entity
+//
+// So we just turn the macro off in this case.
+#if defined(C10_DEFINE_DEPRECATED_USING)
+#undef C10_DEFINE_DEPRECATED_USING
+#endif
+#define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) \
+  using TypeName = TypeThingy;
+#else
+// [[deprecated]] does work in windows without nvcc, though msc doesn't support
+// `__has_cpp_attribute` when c++14 is supported, otherwise
+// __declspec(deprecated) is used as the alternative.
+#ifndef C10_DEFINE_DEPRECATED_USING
+#if defined(_MSVC_LANG) && _MSVC_LANG >= 201402L
+#define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) \
+  using TypeName [[deprecated]] = TypeThingy;
+#else
+#define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) \
+  using TypeName = __declspec(deprecated) TypeThingy;
+#endif
+#endif
+#endif
+#endif
+
+#if !defined(C10_DEFINE_DEPRECATED_USING) && defined(__GNUC__)
+// nvcc has a bug where it doesn't understand __attribute__((deprecated))
+// declarations even when the host compiler supports it. We'll only use this gcc
+// attribute when not cuda, and when using a GCC compiler that doesn't support
+// the c++14 syntax we checked for above (available in __GNUC__ >= 5)
+#if !defined(__CUDACC__)
+#define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) \
+  using TypeName __attribute__((deprecated)) = TypeThingy;
+#else
+// using cuda + gcc < 5, neither deprecated syntax is available so turning off.
+#define C10_DEFINE_DEPRECATED_USING(TypeName, TypeThingy) \
+  using TypeName = TypeThingy;
+#endif
+#endif
+
+#if !defined(C10_DEFINE_DEPRECATED_USING)
+#warning "You need to implement C10_DEFINE_DEPRECATED_USING for this compiler"
+#define C10_DEFINE_DEPRECATED_USING
+#endif

videochat2/lib/python3.10/site-packages/torch/include/c10/util/DimVector.h

@@ -0,0 +1,17 @@
+#pragma once
+
+#include <c10/core/SymInt.h>
+#include <c10/core/impl/SizesAndStrides.h>
+#include <c10/util/SmallVector.h>
+#include <cstddef>
+#include <cstdint>
+
+namespace c10 {
+
+constexpr size_t kDimVectorStaticSize = C10_SIZES_AND_STRIDES_MAX_INLINE_SIZE;
+
+/// A container for sizes or strides
+using DimVector = SmallVector<int64_t, kDimVectorStaticSize>;
+using SymDimVector = SmallVector<c10::SymInt, kDimVectorStaticSize>;
+
+} // namespace c10

videochat2/lib/python3.10/site-packages/torch/include/c10/util/DynamicCounter.h

@@ -0,0 +1,49 @@
+#pragma once
+
+#include <functional>
+#include <memory>
+#include <string_view>
+
+#include <c10/macros/Macros.h>
+
+namespace c10::monitor {
+
+class C10_API DynamicCounter {
+ public:
+  using Callback = std::function<int64_t()>;
+
+  // Creates a dynamic counter that can be queried at any point in time by
+  // multiple backends. Only one counter with a given key can exist at any point
+  // in time.
+  //
+  // The callback is invoked every time the counter is queried.
+  // The callback must be thread-safe.
+  // The callback must not throw.
+  // The callback must not block.
+  DynamicCounter(std::string_view key, Callback getCounterCallback);
+
+  // Unregisters the callback.
+  // Waits for all ongoing callback invocations to finish.
+  ~DynamicCounter();
+
+ private:
+  struct Guard;
+  std::unique_ptr<Guard> guard_;
+};
+
+namespace detail {
+class DynamicCounterBackendIf {
+ public:
+  virtual ~DynamicCounterBackendIf() = default;
+
+  virtual void registerCounter(
+      std::string_view key,
+      DynamicCounter::Callback getCounterCallback) = 0;
+  // MUST wait for all ongoing callback invocations to finish
+  virtual void unregisterCounter(std::string_view key) = 0;
+};
+
+void C10_API
+    registerDynamicCounterBackend(std::unique_ptr<DynamicCounterBackendIf>);
+} // namespace detail
+} // namespace c10::monitor

videochat2/lib/python3.10/site-packages/torch/include/c10/util/FbcodeMaps.h

@@ -0,0 +1,29 @@
+#ifndef C10_UTIL_FBCODEMAPS_H_
+#define C10_UTIL_FBCODEMAPS_H_
+
+// Map typedefs so that we can use folly's F14 maps in fbcode without
+// taking a folly dependency.
+
+#ifdef FBCODE_CAFFE2
+#include <folly/container/F14Map.h>
+#include <folly/container/F14Set.h>
+#else
+#include <unordered_map>
+#include <unordered_set>
+#endif
+
+namespace c10 {
+#ifdef FBCODE_CAFFE2
+template <typename Key, typename Value>
+using FastMap = folly::F14FastMap<Key, Value>;
+template <typename Key>
+using FastSet = folly::F14FastSet<Key>;
+#else
+template <typename Key, typename Value>
+using FastMap = std::unordered_map<Key, Value>;
+template <typename Key>
+using FastSet = std::unordered_set<Key>;
+#endif
+} // namespace c10
+
+#endif // C10_UTIL_FBCODEMAPS_H_

videochat2/lib/python3.10/site-packages/torch/include/c10/util/Flags.h

@@ -0,0 +1,226 @@
+#ifndef C10_UTIL_FLAGS_H_
+#define C10_UTIL_FLAGS_H_
+
+/* Commandline flags support for C10.
+ *
+ * This is a portable commandline flags tool for c10, so we can optionally
+ * choose to use gflags or a lightweight custom implementation if gflags is
+ * not possible on a certain platform. If you have gflags installed, set the
+ * macro C10_USE_GFLAGS will seamlessly route everything to gflags.
+ *
+ * To define a flag foo of type bool default to true, do the following in the
+ * *global* namespace:
+ *     C10_DEFINE_bool(foo, true, "An example.");
+ *
+ * To use it in another .cc file, you can use C10_DECLARE_* as follows:
+ *     C10_DECLARE_bool(foo);
+ *
+ * In both cases, you can then access the flag via FLAGS_foo.
+ *
+ * It is recommended that you build with gflags. To learn more about the flags
+ * usage, refer to the gflags page here:
+ *
+ * https://gflags.github.io/gflags/
+ *
+ * Note about Python users / devs: gflags is initiated from a C++ function
+ * ParseCommandLineFlags, and is usually done in native binaries in the main
+ * function. As Python does not have a modifiable main function, it is usually
+ * difficult to change the flags after Python starts. Hence, it is recommended
+ * that one sets the default value of the flags to one that's acceptable in
+ * general - that will allow Python to run without wrong flags.
+ */
+
+#include <c10/macros/Export.h>
+#include <string>
+
+#include <c10/util/Registry.h>
+
+namespace c10 {
+/**
+ * Sets the usage message when a commandline tool is called with "--help".
+ */
+C10_API void SetUsageMessage(const std::string& str);
+
+/**
+ * Returns the usage message for the commandline tool set by SetUsageMessage.
+ */
+C10_API const char* UsageMessage();
+
+/**
+ * Parses the commandline flags.
+ *
+ * This command parses all the commandline arguments passed in via pargc
+ * and argv. Once it is finished, partc and argv will contain the remaining
+ * commandline args that c10 does not deal with. Note that following
+ * convention, argv[0] contains the binary name and is not parsed.
+ */
+C10_API bool ParseCommandLineFlags(int* pargc, char*** pargv);
+
+/**
+ * Checks if the commandline flags has already been passed.
+ */
+C10_API bool CommandLineFlagsHasBeenParsed();
+
+} // namespace c10
+
+////////////////////////////////////////////////////////////////////////////////
+// Below are gflags and non-gflags specific implementations.
+// In general, they define the following macros for one to declare (use
+// C10_DECLARE) or define (use C10_DEFINE) flags:
+// C10_{DECLARE,DEFINE}_{int,int64,double,bool,string}
+////////////////////////////////////////////////////////////////////////////////
+
+#ifdef C10_USE_GFLAGS
+
+////////////////////////////////////////////////////////////////////////////////
+// Begin gflags section: most functions are basically rerouted to gflags.
+////////////////////////////////////////////////////////////////////////////////
+#include <gflags/gflags.h>
+
+// C10 uses hidden visibility by default. However, in gflags, it only uses
+// export on Windows platform (with dllexport) but not on linux/mac (with
+// default visibility). As a result, to ensure that we are always exporting
+// global variables, we will redefine the GFLAGS_DLL_DEFINE_FLAG macro if we
+// are building C10 as a shared library.
+// This has to be done after the inclusion of gflags, because some early
+// versions of gflags.h (e.g. 2.0 on ubuntu 14.04) directly defines the
+// macros, so we need to do definition after gflags is done.
+#ifdef GFLAGS_DLL_DEFINE_FLAG
+#undef GFLAGS_DLL_DEFINE_FLAG
+#endif // GFLAGS_DLL_DEFINE_FLAG
+#ifdef GFLAGS_DLL_DECLARE_FLAG
+#undef GFLAGS_DLL_DECLARE_FLAG
+#endif // GFLAGS_DLL_DECLARE_FLAG
+#define GFLAGS_DLL_DEFINE_FLAG C10_EXPORT
+#define GFLAGS_DLL_DECLARE_FLAG C10_IMPORT
+
+// gflags before 2.0 uses namespace google and after 2.1 uses namespace gflags.
+// Using GFLAGS_GFLAGS_H_ to capture this change.
+#ifndef GFLAGS_GFLAGS_H_
+namespace gflags = google;
+#endif // GFLAGS_GFLAGS_H_
+
+// Motivation about the gflags wrapper:
+// (1) We would need to make sure that the gflags version and the non-gflags
+// version of C10 are going to expose the same flags abstraction. One should
+// explicitly use FLAGS_flag_name to access the flags.
+// (2) For flag names, it is recommended to start with c10_ to distinguish it
+// from regular gflags flags. For example, do
+//    C10_DEFINE_BOOL(c10_my_flag, true, "An example");
+// to allow one to use FLAGS_c10_my_flag.
+// (3) Gflags has a design issue that does not properly expose the global flags,
+// if one builds the library with -fvisibility=hidden. The current gflags (as of
+// Aug 2018) only deals with the Windows case using dllexport, and not the Linux
+// counterparts. As a result, we will explicitly use C10_EXPORT to export the
+// flags defined in C10. This is done via a global reference, so the flag
+// itself is not duplicated - under the hood it is the same global gflags flag.
+#define C10_GFLAGS_DEF_WRAPPER(type, real_type, name, default_value, help_str) \
+  DEFINE_##type(name, default_value, help_str);
+
+#define C10_DEFINE_int(name, default_value, help_str) \
+  C10_GFLAGS_DEF_WRAPPER(int32, gflags::int32, name, default_value, help_str)
+#define C10_DEFINE_int32(name, default_value, help_str) \
+  C10_DEFINE_int(name, default_value, help_str)
+#define C10_DEFINE_int64(name, default_value, help_str) \
+  C10_GFLAGS_DEF_WRAPPER(int64, gflags::int64, name, default_value, help_str)
+#define C10_DEFINE_double(name, default_value, help_str) \
+  C10_GFLAGS_DEF_WRAPPER(double, double, name, default_value, help_str)
+#define C10_DEFINE_bool(name, default_value, help_str) \
+  C10_GFLAGS_DEF_WRAPPER(bool, bool, name, default_value, help_str)
+#define C10_DEFINE_string(name, default_value, help_str) \
+  C10_GFLAGS_DEF_WRAPPER(string, ::fLS::clstring, name, default_value, help_str)
+
+// DECLARE_typed_var should be used in header files and in the global namespace.
+#define C10_GFLAGS_DECLARE_WRAPPER(type, real_type, name) DECLARE_##type(name);
+
+#define C10_DECLARE_int(name) \
+  C10_GFLAGS_DECLARE_WRAPPER(int32, gflags::int32, name)
+#define C10_DECLARE_int32(name) C10_DECLARE_int(name)
+#define C10_DECLARE_int64(name) \
+  C10_GFLAGS_DECLARE_WRAPPER(int64, gflags::int64, name)
+#define C10_DECLARE_double(name) \
+  C10_GFLAGS_DECLARE_WRAPPER(double, double, name)
+#define C10_DECLARE_bool(name) C10_GFLAGS_DECLARE_WRAPPER(bool, bool, name)
+#define C10_DECLARE_string(name) \
+  C10_GFLAGS_DECLARE_WRAPPER(string, ::fLS::clstring, name)
+
+////////////////////////////////////////////////////////////////////////////////
+// End gflags section.
+////////////////////////////////////////////////////////////////////////////////
+
+#else // C10_USE_GFLAGS
+
+////////////////////////////////////////////////////////////////////////////////
+// Begin non-gflags section: providing equivalent functionality.
+////////////////////////////////////////////////////////////////////////////////
+
+namespace c10 {
+
+class C10_API C10FlagParser {
+ public:
+  bool success() {
+    return success_;
+  }
+
+ protected:
+  template <typename T>
+  bool Parse(const std::string& content, T* value);
+  bool success_{false};
+};
+
+C10_DECLARE_REGISTRY(C10FlagsRegistry, C10FlagParser, const std::string&);
+
+} // namespace c10
+
+// The macros are defined outside the c10 namespace. In your code, you should
+// write the C10_DEFINE_* and C10_DECLARE_* macros outside any namespace
+// as well.
+
+#define C10_DEFINE_typed_var(type, name, default_value, help_str)       \
+  C10_EXPORT type FLAGS_##name = default_value;                         \
+  namespace c10 {                                                       \
+  namespace {                                                           \
+  class C10FlagParser_##name : public C10FlagParser {                   \
+   public:                                                              \
+    explicit C10FlagParser_##name(const std::string& content) {         \
+      success_ = C10FlagParser::Parse<type>(content, &FLAGS_##name);    \
+    }                                                                   \
+  };                                                                    \
+  }                                                                     \
+  RegistererC10FlagsRegistry g_C10FlagsRegistry_##name(                 \
+      #name,                                                            \
+      C10FlagsRegistry(),                                               \
+      RegistererC10FlagsRegistry::DefaultCreator<C10FlagParser_##name>, \
+      "(" #type ", default " #default_value ") " help_str);             \
+  }
+
+#define C10_DEFINE_int(name, default_value, help_str) \
+  C10_DEFINE_typed_var(int, name, default_value, help_str)
+#define C10_DEFINE_int32(name, default_value, help_str) \
+  C10_DEFINE_int(name, default_value, help_str)
+#define C10_DEFINE_int64(name, default_value, help_str) \
+  C10_DEFINE_typed_var(int64_t, name, default_value, help_str)
+#define C10_DEFINE_double(name, default_value, help_str) \
+  C10_DEFINE_typed_var(double, name, default_value, help_str)
+#define C10_DEFINE_bool(name, default_value, help_str) \
+  C10_DEFINE_typed_var(bool, name, default_value, help_str)
+#define C10_DEFINE_string(name, default_value, help_str) \
+  C10_DEFINE_typed_var(std::string, name, default_value, help_str)
+
+// DECLARE_typed_var should be used in header files and in the global namespace.
+#define C10_DECLARE_typed_var(type, name) C10_API extern type FLAGS_##name
+
+#define C10_DECLARE_int(name) C10_DECLARE_typed_var(int, name)
+#define C10_DECLARE_int32(name) C10_DECLARE_int(name)
+#define C10_DECLARE_int64(name) C10_DECLARE_typed_var(int64_t, name)
+#define C10_DECLARE_double(name) C10_DECLARE_typed_var(double, name)
+#define C10_DECLARE_bool(name) C10_DECLARE_typed_var(bool, name)
+#define C10_DECLARE_string(name) C10_DECLARE_typed_var(std::string, name)
+
+////////////////////////////////////////////////////////////////////////////////
+// End non-gflags section.
+////////////////////////////////////////////////////////////////////////////////
+
+#endif // C10_USE_GFLAGS
+
+#endif // C10_UTIL_FLAGS_H_

videochat2/lib/python3.10/site-packages/torch/include/c10/util/Float8_e4m3fn.h

@@ -0,0 +1,240 @@
+#pragma once
+
+/// Defines the Float8_e4m3fn type (8-bit floating-point) including conversions
+/// to standard C types and basic arithmetic operations. Note that arithmetic
+/// operations are implemented by converting to floating point and
+/// performing the operation in float32.
+/// Binary configuration:
+/// s eeee mmm
+/// 1 sign bit
+/// 4 exponent bits
+/// 3 mantissa bits
+/// bias = 7
+///
+/// Implementation based on the paper https://arxiv.org/pdf/2209.05433.pdf
+/// and inspired by Half implementation from pytorch/c10/util/Half.h
+
+#include <c10/macros/Macros.h>
+#include <c10/util/floating_point_utils.h>
+
+#if defined(__cplusplus)
+#include <cmath>
+#include <cstdint>
+#elif !defined(__OPENCL_VERSION__)
+#include <math.h>
+#include <stdint.h>
+#endif
+
+#ifdef _MSC_VER
+#include <intrin.h>
+#endif
+
+#include <climits>
+#include <iostream>
+
+namespace c10 {
+
+namespace detail {
+
+/*
+ * Convert a 8-bit floating-point number in fp8 E4M3FN format, in bit
+ * representation, to a 32-bit floating-point number in IEEE single-precision
+ * format, in bit representation.
+ *
+ * @note The implementation doesn't use any floating-point operations.
+ */
+inline C10_HOST_DEVICE float fp8e4m3fn_to_fp32_value(uint8_t input) {
+  /*
+   * Extend the fp8 E4M3FN number to 32 bits and shift to the
+   * upper part of the 32-bit word:
+   *      +---+----+---+-----------------------------+
+   *      | S |EEEE|MMM|0000 0000 0000 0000 0000 0000|
+   *      +---+----+---+-----------------------------+
+   * Bits  31 27-30 24-26          0-23
+   *
+   * S - sign bit, E - bits of the biased exponent, M - bits of the mantissa, 0
+   * - zero bits.
+   */
+  const uint32_t w = (uint32_t)input << 24;
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = w & UINT32_C(0x80000000);
+  /*
+   * Extract mantissa and biased exponent of the input number into the bits 0-30
+   * of the 32-bit word:
+   *
+   *      +---+----+---+-----------------------------+
+   *      | S |EEEE|MMM|0000 0000 0000 0000 0000 0000|
+   *      +---+----+---+-----------------------------+
+   * Bits  31  27-30 24-26      0-23
+   */
+  const uint32_t nonsign = w & UINT32_C(0x7FFFFFFF);
+  /*
+   * Renorm shift is the number of bits to shift mantissa left to make the
+   * half-precision number normalized. If the initial number is normalized, some
+   * of its high 5 bits (sign == 0 and 4-bit exponent) equals one. In this case
+   * renorm_shift == 0. If the number is denormalize, renorm_shift > 0. Note
+   * that if we shift denormalized nonsign by renorm_shift, the unit bit of
+   * mantissa will shift into exponent, turning the biased exponent into 1, and
+   * making mantissa normalized (i.e. without leading 1).
+   */
+#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__)
+  uint32_t renorm_shift = __clz(nonsign);
+#elif defined(__SYCL_DEVICE_ONLY__)
+  // Note: zero is not a supported input into `__builtin_clz`
+  uint32_t renorm_shift =
+      nonsign != 0 ? __builtin_clz(nonsign) : sizeof(uint32_t) * CHAR_BIT;
+#elif defined(_MSC_VER)
+  unsigned long nonsign_bsr;
+  _BitScanReverse(&nonsign_bsr, (unsigned long)nonsign);
+  uint32_t renorm_shift = (uint32_t)nonsign_bsr ^ 31;
+#else
+  // Note: zero is not a supported input into `__builtin_clz`
+  uint32_t renorm_shift =
+      nonsign != 0 ? __builtin_clz(nonsign) : sizeof(uint32_t) * CHAR_BIT;
+#endif
+  renorm_shift = renorm_shift > 4 ? renorm_shift - 4 : 0;
+  /*
+   * Iff fp8e4m3fn number has all exponent and mantissa bits set to 1,
+   * the addition overflows it into bit 31, and the subsequent shift turns the
+   * high 9 bits into 1. Thus inf_nan_mask == 0x7F800000 if the fp8e4m3fn number
+   * is Nan, 0x00000000 otherwise
+   */
+  const int32_t inf_nan_mask =
+      ((int32_t)(nonsign + 0x01000000) >> 8) & INT32_C(0x7F800000);
+  /*
+   * Iff nonsign is 0, it overflows into 0xFFFFFFFF, turning bit 31
+   * into 1. Otherwise, bit 31 remains 0. The signed shift right by 31
+   * broadcasts bit 31 into all bits of the zero_mask. Thus zero_mask ==
+   * 0xFFFFFFFF if the half-precision number was zero (+0.0h or -0.0h)
+   * 0x00000000 otherwise
+   */
+  const int32_t zero_mask = (int32_t)(nonsign - 1) >> 31;
+  /*
+   * 1. Shift nonsign left by renorm_shift to normalize it (if the input
+   * was denormal)
+   * 2. Shift nonsign right by 4 so the exponent (4 bits originally)
+   * becomes an 8-bit field and 3-bit mantissa shifts into the 3 high
+   * bits of the 23-bit mantissa of IEEE single-precision number.
+   * 3. Add 0x78 to the exponent (starting at bit 23) to compensate the
+   * different in exponent bias (0x7F for single-precision number less 0x07
+   * for fp8e4m3fn number).
+   * 4. Subtract renorm_shift from the exponent (starting at bit 23) to
+   * account for renormalization. As renorm_shift is less than 0x78, this
+   * can be combined with step 3.
+   * 5. Binary OR with inf_nan_mask to turn the exponent into 0xFF if the
+   * input was NaN or infinity.
+   * 6. Binary ANDNOT with zero_mask to turn the mantissa and exponent
+   * into zero if the input was zero.
+   * 7. Combine with the sign of the input number.
+   */
+  uint32_t result = sign |
+      ((((nonsign << renorm_shift >> 4) + ((0x78 - renorm_shift) << 23)) |
+        inf_nan_mask) &
+       ~zero_mask);
+  return fp32_from_bits(result);
+}
+
+/*
+ * Convert a 32-bit floating-point number in IEEE single-precision format to a
+ * 8-bit floating-point number in fp8 E4M3FN format, in bit representation.
+ */
+inline C10_HOST_DEVICE uint8_t fp8e4m3fn_from_fp32_value(float f) {
+  /*
+   * Binary representation of 480.0f, which is the first value
+   * not representable in fp8e4m3fn range:
+   * 0 1111 111 - fp8e4m3fn
+   * 0 10000111 11100000000000000000000 - fp32
+   */
+  constexpr uint32_t fp8_max = UINT32_C(1087) << 20;
+
+  /*
+   * A mask for converting fp32 numbers lower than fp8e4m3fn normal range
+   * into denorm representation
+   * magic number: ((127 - 7) + (23 - 3) + 1)
+   */
+  constexpr uint32_t denorm_mask = UINT32_C(141) << 23;
+
+  uint32_t f_bits = fp32_to_bits(f);
+
+  uint8_t result = 0u;
+
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = f_bits & UINT32_C(0x80000000);
+
+  /*
+   * Set sign bit to 0
+   */
+  f_bits ^= sign;
+
+  if (f_bits >= fp8_max) {
+    // NaN - all exponent and mantissa bits set to 1
+    result = 0x7f;
+  } else {
+    if (f_bits < (UINT32_C(121) << 23)) {
+      // Input number is smaller than 2^(-6), which is the smallest
+      // fp8e4m3fn normal number
+      f_bits =
+          fp32_to_bits(fp32_from_bits(f_bits) + fp32_from_bits(denorm_mask));
+      result = static_cast<uint8_t>(f_bits - denorm_mask);
+    } else {
+      // resulting mantissa is odd
+      uint8_t mant_odd = (f_bits >> 20) & 1;
+
+      // update exponent, rounding bias part 1
+      f_bits += ((uint32_t)(7 - 127) << 23) + 0x7FFFF;
+
+      // rounding bias part 2
+      f_bits += mant_odd;
+
+      // take the bits!
+      result = static_cast<uint8_t>(f_bits >> 20);
+    }
+  }
+
+  result |= static_cast<uint8_t>(sign >> 24);
+  return result;
+}
+
+} // namespace detail
+
+struct alignas(1) Float8_e4m3fn {
+  uint8_t x;
+
+  struct from_bits_t {};
+  C10_HOST_DEVICE static constexpr from_bits_t from_bits() {
+    return from_bits_t();
+  }
+
+  Float8_e4m3fn() = default;
+
+  constexpr C10_HOST_DEVICE Float8_e4m3fn(uint8_t bits, from_bits_t)
+      : x(bits) {}
+  inline C10_HOST_DEVICE Float8_e4m3fn(float value);
+  inline C10_HOST_DEVICE operator float() const;
+  inline C10_HOST_DEVICE bool isnan() const;
+};
+
+C10_API inline std::ostream& operator<<(
+    std::ostream& out,
+    const Float8_e4m3fn& value) {
+  out << (float)value;
+  return out;
+}
+
+} // namespace c10
+
+#include <c10/util/Float8_e4m3fn-inl.h> // IWYU pragma: keep

videochat2/lib/python3.10/site-packages/torch/include/c10/util/Float8_e4m3fnuz.h

@@ -0,0 +1,139 @@
+#pragma once
+
+/// Defines the Float8_e4m3fnuz type (8-bit floating-point) including
+/// conversions to standard C types and basic arithmetic operations. Note that
+/// arithmetic operations are implemented by converting to floating point and
+/// performing the operation in float32.
+/// Binary configuration remains the same as Float8_e4m3fn:
+/// s eeee mmm
+/// 1 sign bit
+/// 4 exponent bits
+/// 3 mantissa bits
+/// The key differences versus Float8_e4m3fn are:
+/// bias = 8
+/// no infinities or negative zero
+/// NaN only when sign bit is 1, rest all 0s
+///
+/// Implementation based on the paper https://arxiv.org/pdf/2206.02915.pdf and
+/// the existing Float8_e4m3fn implementation.
+
+#include <c10/macros/Export.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/floating_point_utils.h>
+#include <type_traits>
+
+#if defined(__cplusplus)
+#include <cstdint>
+#elif !defined(__OPENCL_VERSION__)
+#include <math.h>
+#include <stdint.h>
+#endif
+
+#include <iosfwd>
+#include <ostream>
+
+namespace c10 {
+
+namespace detail {
+
+/*
+ * Convert a 32-bit floating-point number in IEEE single-precision format to a
+ * 8-bit floating-point number in fp8 E4M3FNUZ format, in bit representation.
+ */
+inline C10_HOST_DEVICE uint8_t fp8e4m3fnuz_from_fp32_value(float f) {
+  /*
+   * Binary representation of 256.0f, which is the first value not representable
+   * (i.e. the first value which would overflow in to the sign bit, resulting in
+   * a NaN) in fp8e4m3fnuz range:
+   * 1 0000 000 - fp8e4m3fnuz
+   * 0 10000111 00000000000000000000000 - fp32
+   */
+  constexpr uint32_t fnuz_max = UINT32_C(0x87) << 23;
+
+  /*
+   * A mask for converting fp32 numbers lower than fp8e4m3fnuz normal range
+   * into denorm representation
+   * magic number: ((127 - 8) + (23 - 3) + 1)
+   */
+  constexpr uint32_t denorm_mask = UINT32_C(0x8C) << 23;
+
+  uint32_t f_bits = fp32_to_bits(f);
+
+  uint32_t result = 0u;
+
+  /*
+   * Extract the sign of the input number into the high bit of the 32-bit word:
+   *
+   *      +---+----------------------------------+
+   *      | S |0000000 00000000 00000000 00000000|
+   *      +---+----------------------------------+
+   * Bits  31                 0-31
+   */
+  const uint32_t sign = f_bits & UINT32_C(0x80000000);
+
+  /*
+   * Set sign bit to 0
+   */
+  f_bits ^= sign;
+
+  if (f_bits >= fnuz_max) {
+    // NaN -- sign bit set to 1, rest 0s.
+    return 0x80;
+  }
+
+  if (f_bits < (UINT32_C(0x78) << 23) /* 2^-7 in float32 */) {
+    // Input exponent is less than -7, the smallest e4m3fnuz exponent, so the
+    // number will become subnormal.
+    f_bits = fp32_to_bits(fp32_from_bits(f_bits) + fp32_from_bits(denorm_mask));
+    result = static_cast<uint8_t>(f_bits - denorm_mask);
+    if (result == 0) {
+      // fnuz types don't have negative zero.
+      return 0;
+    }
+  } else {
+    // resulting mantissa is odd
+    uint8_t mant_odd = (f_bits >> 20) & 1;
+
+    // update exponent, rounding bias part 1
+    f_bits += ((uint32_t)(8 - 127) << 23) + 0x7FFFF;
+
+    // rounding bias part 2
+    f_bits += mant_odd;
+
+    // take the bits!
+    result = static_cast<uint8_t>(f_bits >> 20);
+  }
+
+  result |= sign >> 24;
+  return result;
+}
+
+} // namespace detail
+
+struct alignas(1) Float8_e4m3fnuz {
+  uint8_t x;
+
+  struct from_bits_t {};
+  C10_HOST_DEVICE static constexpr from_bits_t from_bits() {
+    return from_bits_t();
+  }
+
+  Float8_e4m3fnuz() = default;
+
+  constexpr C10_HOST_DEVICE Float8_e4m3fnuz(uint8_t bits, from_bits_t)
+      : x(bits) {}
+  inline C10_HOST_DEVICE Float8_e4m3fnuz(float value);
+  inline C10_HOST_DEVICE operator float() const;
+  inline C10_HOST_DEVICE bool isnan() const;
+};
+
+C10_API inline std::ostream& operator<<(
+    std::ostream& out,
+    const Float8_e4m3fnuz& value) {
+  out << (float)value;
+  return out;
+}
+
+} // namespace c10
+
+#include <c10/util/Float8_e4m3fnuz-inl.h> // IWYU pragma: keep

videochat2/lib/python3.10/site-packages/torch/include/c10/util/Float8_e5m2-inl.h

@@ -0,0 +1,286 @@
+#pragma once
+
+#include <c10/macros/Macros.h>
+#include <cstring>
+#include <limits>
+
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+#define EXP_WIDTH_FP8 5
+#define MAN_WIDTH_FP8 2
+#define EXP_BIAS_FP8 15
+
+namespace c10 {
+
+/// Constructors
+
+inline C10_HOST_DEVICE Float8_e5m2::Float8_e5m2(float value)
+    : x(detail::fp8e5m2_from_fp32_value(value)) {}
+
+/// Implicit conversions
+
+inline C10_HOST_DEVICE Float8_e5m2::operator float() const {
+  return detail::fp8e5m2_to_fp32_value(x);
+}
+
+/// Special values helpers
+
+inline C10_HOST_DEVICE bool Float8_e5m2::isnan() const {
+  return (x & 0b01111111) > 0b01111100;
+}
+
+inline C10_HOST_DEVICE bool Float8_e5m2::isinf() const {
+  return (x & 0b01111111) == 0b01111100;
+}
+
+/// Arithmetic
+
+inline C10_HOST_DEVICE Float8_e5m2
+operator+(const Float8_e5m2& a, const Float8_e5m2& b) {
+  return static_cast<float>(a) + static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2
+operator-(const Float8_e5m2& a, const Float8_e5m2& b) {
+  return static_cast<float>(a) - static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2
+operator*(const Float8_e5m2& a, const Float8_e5m2& b) {
+  return static_cast<float>(a) * static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2 operator/(
+    const Float8_e5m2& a,
+    const Float8_e5m2& b) __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2 operator-(const Float8_e5m2& a) {
+  return -static_cast<float>(a);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2& operator+=(
+    Float8_e5m2& a,
+    const Float8_e5m2& b) {
+  a = a + b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e5m2& operator-=(
+    Float8_e5m2& a,
+    const Float8_e5m2& b) {
+  a = a - b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e5m2& operator*=(
+    Float8_e5m2& a,
+    const Float8_e5m2& b) {
+  a = a * b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e5m2& operator/=(
+    Float8_e5m2& a,
+    const Float8_e5m2& b) {
+  a = a / b;
+  return a;
+}
+
+/// Arithmetic with floats
+
+inline C10_HOST_DEVICE float operator+(Float8_e5m2 a, float b) {
+  return static_cast<float>(a) + b;
+}
+inline C10_HOST_DEVICE float operator-(Float8_e5m2 a, float b) {
+  return static_cast<float>(a) - b;
+}
+inline C10_HOST_DEVICE float operator*(Float8_e5m2 a, float b) {
+  return static_cast<float>(a) * b;
+}
+inline C10_HOST_DEVICE float operator/(Float8_e5m2 a, float b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / b;
+}
+
+inline C10_HOST_DEVICE float operator+(float a, Float8_e5m2 b) {
+  return a + static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator-(float a, Float8_e5m2 b) {
+  return a - static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator*(float a, Float8_e5m2 b) {
+  return a * static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator/(float a, Float8_e5m2 b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE float& operator+=(float& a, const Float8_e5m2& b) {
+  return a += static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator-=(float& a, const Float8_e5m2& b) {
+  return a -= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator*=(float& a, const Float8_e5m2& b) {
+  return a *= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator/=(float& a, const Float8_e5m2& b) {
+  return a /= static_cast<float>(b);
+}
+
+/// Arithmetic with doubles
+
+inline C10_HOST_DEVICE double operator+(Float8_e5m2 a, double b) {
+  return static_cast<double>(a) + b;
+}
+inline C10_HOST_DEVICE double operator-(Float8_e5m2 a, double b) {
+  return static_cast<double>(a) - b;
+}
+inline C10_HOST_DEVICE double operator*(Float8_e5m2 a, double b) {
+  return static_cast<double>(a) * b;
+}
+inline C10_HOST_DEVICE double operator/(Float8_e5m2 a, double b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<double>(a) / b;
+}
+
+inline C10_HOST_DEVICE double operator+(double a, Float8_e5m2 b) {
+  return a + static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator-(double a, Float8_e5m2 b) {
+  return a - static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator*(double a, Float8_e5m2 b) {
+  return a * static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator/(double a, Float8_e5m2 b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<double>(b);
+}
+
+/// Arithmetic with ints
+
+inline C10_HOST_DEVICE Float8_e5m2 operator+(Float8_e5m2 a, int b) {
+  return a + static_cast<Float8_e5m2>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator-(Float8_e5m2 a, int b) {
+  return a - static_cast<Float8_e5m2>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator*(Float8_e5m2 a, int b) {
+  return a * static_cast<Float8_e5m2>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator/(Float8_e5m2 a, int b) {
+  return a / static_cast<Float8_e5m2>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2 operator+(int a, Float8_e5m2 b) {
+  return static_cast<Float8_e5m2>(a) + b;
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator-(int a, Float8_e5m2 b) {
+  return static_cast<Float8_e5m2>(a) - b;
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator*(int a, Float8_e5m2 b) {
+  return static_cast<Float8_e5m2>(a) * b;
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator/(int a, Float8_e5m2 b) {
+  return static_cast<Float8_e5m2>(a) / b;
+}
+
+//// Arithmetic with int64_t
+
+inline C10_HOST_DEVICE Float8_e5m2 operator+(Float8_e5m2 a, int64_t b) {
+  return a + static_cast<Float8_e5m2>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator-(Float8_e5m2 a, int64_t b) {
+  return a - static_cast<Float8_e5m2>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator*(Float8_e5m2 a, int64_t b) {
+  return a * static_cast<Float8_e5m2>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator/(Float8_e5m2 a, int64_t b) {
+  return a / static_cast<Float8_e5m2>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2 operator+(int64_t a, Float8_e5m2 b) {
+  return static_cast<Float8_e5m2>(a) + b;
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator-(int64_t a, Float8_e5m2 b) {
+  return static_cast<Float8_e5m2>(a) - b;
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator*(int64_t a, Float8_e5m2 b) {
+  return static_cast<Float8_e5m2>(a) * b;
+}
+inline C10_HOST_DEVICE Float8_e5m2 operator/(int64_t a, Float8_e5m2 b) {
+  return static_cast<Float8_e5m2>(a) / b;
+}
+
+/// NOTE: we do not define comparisons directly and instead rely on the implicit
+/// conversion from c10::Float8_e5m2 to float.
+
+} // namespace c10
+
+namespace std {
+
+template <>
+class numeric_limits<c10::Float8_e5m2> {
+ public:
+  static constexpr bool is_signed = true;
+  static constexpr bool is_integer = false;
+  static constexpr bool is_specialized = true;
+  static constexpr bool is_exact = false;
+  static constexpr bool has_infinity = true;
+  static constexpr bool has_quiet_NaN = true;
+  static constexpr bool has_signaling_NaN = false;
+  static constexpr auto has_denorm = true;
+  static constexpr auto has_denorm_loss = true;
+  static constexpr auto round_style = numeric_limits<float>::round_style;
+  static constexpr bool is_iec559 = false;
+  static constexpr bool is_bounded = true;
+  static constexpr bool is_modulo = false;
+  static constexpr int digits = 3;
+  static constexpr int digits10 = 0;
+  static constexpr int max_digits10 = 2;
+  static constexpr int radix = 2;
+  static constexpr int min_exponent = -13;
+  static constexpr int min_exponent10 = -4;
+  static constexpr int max_exponent = 16;
+  static constexpr int max_exponent10 = 4;
+  static constexpr auto traps = numeric_limits<float>::traps;
+  static constexpr auto tinyness_before =
+      numeric_limits<float>::tinyness_before;
+
+  static constexpr c10::Float8_e5m2 min() {
+    return c10::Float8_e5m2(0x4, c10::Float8_e5m2::from_bits());
+  }
+  static constexpr c10::Float8_e5m2 max() {
+    return c10::Float8_e5m2(0x7B, c10::Float8_e5m2::from_bits());
+  }
+  static constexpr c10::Float8_e5m2 lowest() {
+    return c10::Float8_e5m2(0xFB, c10::Float8_e5m2::from_bits());
+  }
+  static constexpr c10::Float8_e5m2 epsilon() {
+    return c10::Float8_e5m2(0x34, c10::Float8_e5m2::from_bits());
+  }
+  static constexpr c10::Float8_e5m2 round_error() {
+    return c10::Float8_e5m2(0x38, c10::Float8_e5m2::from_bits());
+  }
+  static constexpr c10::Float8_e5m2 infinity() {
+    return c10::Float8_e5m2(0x7C, c10::Float8_e5m2::from_bits());
+  }
+  static constexpr c10::Float8_e5m2 quiet_NaN() {
+    return c10::Float8_e5m2(0x7F, c10::Float8_e5m2::from_bits());
+  }
+  static constexpr c10::Float8_e5m2 denorm_min() {
+    return c10::Float8_e5m2(0x01, c10::Float8_e5m2::from_bits());
+  }
+};
+
+} // namespace std
+
+C10_CLANG_DIAGNOSTIC_POP()

videochat2/lib/python3.10/site-packages/torch/include/c10/util/Float8_e5m2fnuz-inl.h

@@ -0,0 +1,285 @@
+#pragma once
+
+#include <c10/macros/Macros.h>
+#include <c10/util/Float8_fnuz_cvt.h>
+#include <cstring>
+#include <limits>
+
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+namespace c10 {
+
+/// Constructors
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz::Float8_e5m2fnuz(float value)
+    : x(detail::fp8e5m2fnuz_from_fp32_value(value)) {}
+
+/// Implicit conversions
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz::operator float() const {
+  return detail::fp8_fnuz_to_fp32_value<5, 2>(x);
+}
+
+/// Special values helpers
+
+inline C10_HOST_DEVICE bool Float8_e5m2fnuz::isnan() const {
+  return x == 0b10000000;
+}
+
+inline C10_HOST_DEVICE bool Float8_e5m2fnuz::isinf() const {
+  return false;
+}
+
+/// Arithmetic
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz
+operator+(const Float8_e5m2fnuz& a, const Float8_e5m2fnuz& b) {
+  return static_cast<float>(a) + static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz
+operator-(const Float8_e5m2fnuz& a, const Float8_e5m2fnuz& b) {
+  return static_cast<float>(a) - static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz
+operator*(const Float8_e5m2fnuz& a, const Float8_e5m2fnuz& b) {
+  return static_cast<float>(a) * static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator/(
+    const Float8_e5m2fnuz& a,
+    const Float8_e5m2fnuz& b) __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator-(const Float8_e5m2fnuz& a) {
+  return -static_cast<float>(a);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz& operator+=(
+    Float8_e5m2fnuz& a,
+    const Float8_e5m2fnuz& b) {
+  a = a + b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz& operator-=(
+    Float8_e5m2fnuz& a,
+    const Float8_e5m2fnuz& b) {
+  a = a - b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz& operator*=(
+    Float8_e5m2fnuz& a,
+    const Float8_e5m2fnuz& b) {
+  a = a * b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz& operator/=(
+    Float8_e5m2fnuz& a,
+    const Float8_e5m2fnuz& b) {
+  a = a / b;
+  return a;
+}
+
+/// Arithmetic with floats
+
+inline C10_HOST_DEVICE float operator+(Float8_e5m2fnuz a, float b) {
+  return static_cast<float>(a) + b;
+}
+inline C10_HOST_DEVICE float operator-(Float8_e5m2fnuz a, float b) {
+  return static_cast<float>(a) - b;
+}
+inline C10_HOST_DEVICE float operator*(Float8_e5m2fnuz a, float b) {
+  return static_cast<float>(a) * b;
+}
+inline C10_HOST_DEVICE float operator/(Float8_e5m2fnuz a, float b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / b;
+}
+
+inline C10_HOST_DEVICE float operator+(float a, Float8_e5m2fnuz b) {
+  return a + static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator-(float a, Float8_e5m2fnuz b) {
+  return a - static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator*(float a, Float8_e5m2fnuz b) {
+  return a * static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator/(float a, Float8_e5m2fnuz b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE float& operator+=(float& a, const Float8_e5m2fnuz& b) {
+  return a += static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator-=(float& a, const Float8_e5m2fnuz& b) {
+  return a -= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator*=(float& a, const Float8_e5m2fnuz& b) {
+  return a *= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator/=(float& a, const Float8_e5m2fnuz& b) {
+  return a /= static_cast<float>(b);
+}
+
+/// Arithmetic with doubles
+
+inline C10_HOST_DEVICE double operator+(Float8_e5m2fnuz a, double b) {
+  return static_cast<double>(a) + b;
+}
+inline C10_HOST_DEVICE double operator-(Float8_e5m2fnuz a, double b) {
+  return static_cast<double>(a) - b;
+}
+inline C10_HOST_DEVICE double operator*(Float8_e5m2fnuz a, double b) {
+  return static_cast<double>(a) * b;
+}
+inline C10_HOST_DEVICE double operator/(Float8_e5m2fnuz a, double b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<double>(a) / b;
+}
+
+inline C10_HOST_DEVICE double operator+(double a, Float8_e5m2fnuz b) {
+  return a + static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator-(double a, Float8_e5m2fnuz b) {
+  return a - static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator*(double a, Float8_e5m2fnuz b) {
+  return a * static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator/(double a, Float8_e5m2fnuz b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<double>(b);
+}
+
+/// Arithmetic with ints
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator+(Float8_e5m2fnuz a, int b) {
+  return a + static_cast<Float8_e5m2fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator-(Float8_e5m2fnuz a, int b) {
+  return a - static_cast<Float8_e5m2fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator*(Float8_e5m2fnuz a, int b) {
+  return a * static_cast<Float8_e5m2fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator/(Float8_e5m2fnuz a, int b) {
+  return a / static_cast<Float8_e5m2fnuz>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator+(int a, Float8_e5m2fnuz b) {
+  return static_cast<Float8_e5m2fnuz>(a) + b;
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator-(int a, Float8_e5m2fnuz b) {
+  return static_cast<Float8_e5m2fnuz>(a) - b;
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator*(int a, Float8_e5m2fnuz b) {
+  return static_cast<Float8_e5m2fnuz>(a) * b;
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator/(int a, Float8_e5m2fnuz b) {
+  return static_cast<Float8_e5m2fnuz>(a) / b;
+}
+
+//// Arithmetic with int64_t
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator+(Float8_e5m2fnuz a, int64_t b) {
+  return a + static_cast<Float8_e5m2fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator-(Float8_e5m2fnuz a, int64_t b) {
+  return a - static_cast<Float8_e5m2fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator*(Float8_e5m2fnuz a, int64_t b) {
+  return a * static_cast<Float8_e5m2fnuz>(b);
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator/(Float8_e5m2fnuz a, int64_t b) {
+  return a / static_cast<Float8_e5m2fnuz>(b);
+}
+
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator+(int64_t a, Float8_e5m2fnuz b) {
+  return static_cast<Float8_e5m2fnuz>(a) + b;
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator-(int64_t a, Float8_e5m2fnuz b) {
+  return static_cast<Float8_e5m2fnuz>(a) - b;
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator*(int64_t a, Float8_e5m2fnuz b) {
+  return static_cast<Float8_e5m2fnuz>(a) * b;
+}
+inline C10_HOST_DEVICE Float8_e5m2fnuz operator/(int64_t a, Float8_e5m2fnuz b) {
+  return static_cast<Float8_e5m2fnuz>(a) / b;
+}
+
+/// NOTE: we do not define comparisons directly and instead rely on the implicit
+/// conversion from c10::Float8_e5m2fnuz to float.
+
+} // namespace c10
+
+namespace std {
+
+template <>
+class numeric_limits<c10::Float8_e5m2fnuz> {
+ public:
+  static constexpr bool is_signed = true;
+  static constexpr bool is_integer = false;
+  static constexpr bool is_specialized = true;
+  static constexpr bool is_exact = false;
+  static constexpr bool has_infinity = false;
+  static constexpr bool has_quiet_NaN = true;
+  static constexpr bool has_signaling_NaN = false;
+  static constexpr auto has_denorm = true;
+  static constexpr auto has_denorm_loss = true;
+  static constexpr auto round_style = numeric_limits<float>::round_style;
+  static constexpr bool is_iec559 = false;
+  static constexpr bool is_bounded = true;
+  static constexpr bool is_modulo = false;
+  static constexpr int digits = 3;
+  static constexpr int digits10 = 0;
+  static constexpr int max_digits10 = 2;
+  static constexpr int radix = 2;
+  static constexpr int min_exponent = -14;
+  static constexpr int min_exponent10 = -4;
+  static constexpr int max_exponent = 16;
+  static constexpr int max_exponent10 = 4;
+  static constexpr auto traps = numeric_limits<float>::traps;
+  static constexpr auto tinyness_before =
+      numeric_limits<float>::tinyness_before;
+
+  static constexpr c10::Float8_e5m2fnuz min() {
+    return c10::Float8_e5m2fnuz(0x04, c10::Float8_e5m2fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e5m2fnuz max() {
+    return c10::Float8_e5m2fnuz(0x7F, c10::Float8_e5m2fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e5m2fnuz lowest() {
+    return c10::Float8_e5m2fnuz(0xFF, c10::Float8_e5m2fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e5m2fnuz epsilon() {
+    return c10::Float8_e5m2fnuz(0x34, c10::Float8_e5m2fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e5m2fnuz round_error() {
+    return c10::Float8_e5m2fnuz(0x38, c10::Float8_e5m2fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e5m2fnuz infinity() {
+    return c10::Float8_e5m2fnuz(0x80, c10::Float8_e5m2fnuz::from_bits());
+  }
+  // TODO(future): we are mapping neg_zero to both inf and NaN, this is
+  // surprising and we should figure out what to do about it.
+  static constexpr c10::Float8_e5m2fnuz quiet_NaN() {
+    return c10::Float8_e5m2fnuz(0x80, c10::Float8_e5m2fnuz::from_bits());
+  }
+  static constexpr c10::Float8_e5m2fnuz denorm_min() {
+    return c10::Float8_e5m2fnuz(0x01, c10::Float8_e5m2fnuz::from_bits());
+  }
+};
+
+} // namespace std
+
+C10_CLANG_DIAGNOSTIC_POP()

videochat2/lib/python3.10/site-packages/torch/include/c10/util/Float8_fnuz_cvt.h

@@ -0,0 +1,64 @@
+#pragma once
+
+#include <c10/util/floating_point_utils.h>
+
+#include <cstdint>
+
+#if defined(SYCL_LANGUAGE_VERSION)
+#include <sycl/sycl.hpp>
+#endif
+
+namespace c10::detail {
+
+/*
+ * Convert a 8-bit floating-point number in either f8 E4M3FNUZ or bf8 E5M2FNUZ
+ * format, in bit representation, to a 32-bit floating-point number.
+ */
+template <uint32_t we, uint32_t wm>
+inline C10_HOST_DEVICE float fp8_fnuz_to_fp32_value(uint8_t x) {
+  static_assert((we == 4 && wm == 3) || (we == 5 && wm == 2));
+  constexpr uint32_t weo = 8;
+  constexpr uint32_t wmo = 23;
+
+  if (x == 0) {
+    return 0;
+  }
+
+  if (x == 0x80) {
+    constexpr uint32_t ifNaN = 0x7F800001;
+    return fp32_from_bits(ifNaN);
+  }
+
+  uint32_t mantissa = x & ((1 << wm) - 1);
+  uint32_t exponent = (x & 0x7F) >> wm;
+
+  // subnormal input
+  if (exponent == 0) {
+    // guaranteed mantissa!=0 since cases 0x0 and 0x80 are handled above
+#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__)
+    uint32_t renorm_shift = __clz(mantissa);
+#elif defined(__SYCL_DEVICE_ONLY__)
+    uint32_t renorm_shift = sycl::clz(mantissa);
+#elif defined(_MSC_VER)
+    unsigned long nonsign_bsr;
+    _BitScanReverse(&nonsign_bsr, (unsigned long)mantissa);
+    uint32_t renorm_shift = (uint32_t)nonsign_bsr ^ 31;
+#else
+    uint32_t renorm_shift = __builtin_clz(mantissa);
+#endif
+    uint32_t sh = 1 + renorm_shift - (32 - wm);
+    mantissa <<= sh;
+    exponent += 1 - sh;
+    mantissa &= ((1 << wm) - 1);
+  }
+
+  const uint32_t exp_low_cutoff = (1 << (weo - 1)) - (1 << (we - 1));
+  exponent += exp_low_cutoff - 1;
+  mantissa <<= wmo - wm;
+
+  uint32_t sign = x >> 7;
+  uint32_t retval = (sign << 31) | (exponent << 23) | mantissa;
+  return fp32_from_bits(retval);
+}
+
+} // namespace c10::detail

videochat2/lib/python3.10/site-packages/torch/include/c10/util/FunctionRef.h

@@ -0,0 +1,73 @@
+//===- llvm/ADT/STLExtras.h - Useful STL related functions ------*- C++ -*-===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+//
+// This file contains some templates that are useful if you are working with the
+// STL at all.
+//
+// No library is required when using these functions.
+//
+//===----------------------------------------------------------------------===//
+
+// c10: modified from llvm::function_ref
+// c10: added more SFINAE to enable use in overloaded functions
+
+#pragma once
+
+#include <cstdint>
+#include <type_traits>
+#include <utility>
+
+namespace c10 {
+
+/// An efficient, type-erasing, non-owning reference to a callable. This is
+/// intended for use as the type of a function parameter that is not used
+/// after the function in question returns.
+///
+/// This class does not own the callable, so it is not in general safe to store
+/// a function_ref.
+template <typename Fn>
+class function_ref;
+
+template <typename Ret, typename... Params>
+class function_ref<Ret(Params...)> {
+  Ret (*callback)(intptr_t callable, Params... params) = nullptr;
+  intptr_t callable{};
+
+  template <typename Callable>
+  static Ret callback_fn(intptr_t callable, Params... params) {
+    return (*reinterpret_cast<Callable*>(callable))(
+        std::forward<Params>(params)...);
+  }
+
+ public:
+  function_ref() = default;
+  function_ref(std::nullptr_t) {}
+
+  template <typename Callable>
+  function_ref(
+      // NOLINTNEXTLINE(cppcoreguidelines-missing-std-forward)
+      Callable&& callable,
+      std::enable_if_t<
+          !std::is_same_v<std::remove_reference_t<Callable>, function_ref>>* =
+          nullptr,
+      std::enable_if_t<std::is_convertible_v<
+          typename std::invoke_result_t<Callable, Params...>,
+          Ret>>* = nullptr)
+      : callback(callback_fn<std::remove_reference_t<Callable>>),
+        callable(reinterpret_cast<intptr_t>(&callable)) {}
+
+  Ret operator()(Params... params) const {
+    return callback(callable, std::forward<Params>(params)...);
+  }
+
+  operator bool() const {
+    return callback;
+  }
+};
+
+} // namespace c10

videochat2/lib/python3.10/site-packages/torch/include/c10/util/Half-inl.h

@@ -0,0 +1,350 @@
+#pragma once
+
+#include <c10/macros/Macros.h>
+#include <c10/util/bit_cast.h>
+
+#include <cstring>
+#include <limits>
+
+#ifdef __CUDACC__
+#include <cuda_fp16.h>
+#endif
+
+#ifdef __HIPCC__
+#include <hip/hip_fp16.h>
+#endif
+
+#if defined(CL_SYCL_LANGUAGE_VERSION)
+#include <CL/sycl.hpp> // for SYCL 1.2.1
+#elif defined(SYCL_LANGUAGE_VERSION)
+#include <sycl/sycl.hpp> // for SYCL 2020
+#endif
+
+#if (defined(CPU_CAPABILITY_AVX2) || defined(CPU_CAPABILITY_AVX512)) && \
+    !defined(__APPLE__)
+#include <ATen/cpu/vec/vec_half.h>
+#endif
+
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+namespace c10 {
+
+#if defined(__aarch64__) && !defined(__CUDACC__)
+/// Constructors
+inline Half::Half(float16_t value) : x(detail::fp16_to_bits(value)) {}
+inline Half::operator float16_t() const {
+  return detail::fp16_from_bits(x);
+}
+#else
+
+inline C10_HOST_DEVICE Half::Half(float value)
+    :
+#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__)
+      x(__half_as_short(__float2half(value)))
+#elif defined(__SYCL_DEVICE_ONLY__)
+      x(c10::bit_cast<uint16_t>(sycl::half(value)))
+#elif (defined(CPU_CAPABILITY_AVX2) || defined(CPU_CAPABILITY_AVX512)) && \
+    !defined(__APPLE__)
+      x(at::vec::float2half_scalar(value))
+#else
+      x(detail::fp16_ieee_from_fp32_value(value))
+#endif
+{
+}
+
+/// Implicit conversions
+
+inline C10_HOST_DEVICE Half::operator float() const {
+#if defined(__CUDA_ARCH__) || defined(__HIP_DEVICE_COMPILE__)
+  return __half2float(*reinterpret_cast<const __half*>(&x));
+#elif defined(__SYCL_DEVICE_ONLY__)
+  return float(c10::bit_cast<sycl::half>(x));
+#elif (defined(CPU_CAPABILITY_AVX2) || defined(CPU_CAPABILITY_AVX512)) && \
+    !defined(__APPLE__)
+  return at::vec::half2float_scalar(x);
+#elif defined(__aarch64__) && !defined(__CUDACC__)
+  return detail::native_fp16_to_fp32_value(x);
+#else
+  return detail::fp16_ieee_to_fp32_value(x);
+#endif
+}
+
+#endif /* !defined(__aarch64__) || defined(__CUDACC__) \
+        */
+
+#if defined(__CUDACC__) || defined(__HIPCC__)
+inline C10_HOST_DEVICE Half::Half(const __half& value) {
+  x = *reinterpret_cast<const unsigned short*>(&value);
+}
+inline C10_HOST_DEVICE Half::operator __half() const {
+  return *reinterpret_cast<const __half*>(&x);
+}
+#endif
+
+#ifdef SYCL_LANGUAGE_VERSION
+inline C10_HOST_DEVICE Half::Half(const sycl::half& value) {
+  x = *reinterpret_cast<const unsigned short*>(&value);
+}
+inline C10_HOST_DEVICE Half::operator sycl::half() const {
+  return *reinterpret_cast<const sycl::half*>(&x);
+}
+#endif
+
+// CUDA intrinsics
+
+#if (defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 350)) || \
+    (defined(__clang__) && defined(__CUDA__))
+inline __device__ Half __ldg(const Half* ptr) {
+  return __ldg(reinterpret_cast<const __half*>(ptr));
+}
+#endif
+
+/// Arithmetic
+
+inline C10_HOST_DEVICE Half operator+(const Half& a, const Half& b) {
+  return static_cast<float>(a) + static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Half operator-(const Half& a, const Half& b) {
+  return static_cast<float>(a) - static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Half operator*(const Half& a, const Half& b) {
+  return static_cast<float>(a) * static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Half operator/(const Half& a, const Half& b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE Half operator-(const Half& a) {
+#if (defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530) || \
+    defined(__HIP_DEVICE_COMPILE__)
+  return __hneg(a);
+#elif defined(__SYCL_DEVICE_ONLY__)
+  return -c10::bit_cast<sycl::half>(a);
+#else
+  return -static_cast<float>(a);
+#endif
+}
+
+inline C10_HOST_DEVICE Half& operator+=(Half& a, const Half& b) {
+  a = a + b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Half& operator-=(Half& a, const Half& b) {
+  a = a - b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Half& operator*=(Half& a, const Half& b) {
+  a = a * b;
+  return a;
+}
+
+inline C10_HOST_DEVICE Half& operator/=(Half& a, const Half& b) {
+  a = a / b;
+  return a;
+}
+
+/// Arithmetic with floats
+
+inline C10_HOST_DEVICE float operator+(Half a, float b) {
+  return static_cast<float>(a) + b;
+}
+inline C10_HOST_DEVICE float operator-(Half a, float b) {
+  return static_cast<float>(a) - b;
+}
+inline C10_HOST_DEVICE float operator*(Half a, float b) {
+  return static_cast<float>(a) * b;
+}
+inline C10_HOST_DEVICE float operator/(Half a, float b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<float>(a) / b;
+}
+
+inline C10_HOST_DEVICE float operator+(float a, Half b) {
+  return a + static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator-(float a, Half b) {
+  return a - static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator*(float a, Half b) {
+  return a * static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float operator/(float a, Half b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<float>(b);
+}
+
+inline C10_HOST_DEVICE float& operator+=(float& a, const Half& b) {
+  return a += static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator-=(float& a, const Half& b) {
+  return a -= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator*=(float& a, const Half& b) {
+  return a *= static_cast<float>(b);
+}
+inline C10_HOST_DEVICE float& operator/=(float& a, const Half& b) {
+  return a /= static_cast<float>(b);
+}
+
+/// Arithmetic with doubles
+
+inline C10_HOST_DEVICE double operator+(Half a, double b) {
+  return static_cast<double>(a) + b;
+}
+inline C10_HOST_DEVICE double operator-(Half a, double b) {
+  return static_cast<double>(a) - b;
+}
+inline C10_HOST_DEVICE double operator*(Half a, double b) {
+  return static_cast<double>(a) * b;
+}
+inline C10_HOST_DEVICE double operator/(Half a, double b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return static_cast<double>(a) / b;
+}
+
+inline C10_HOST_DEVICE double operator+(double a, Half b) {
+  return a + static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator-(double a, Half b) {
+  return a - static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator*(double a, Half b) {
+  return a * static_cast<double>(b);
+}
+inline C10_HOST_DEVICE double operator/(double a, Half b)
+    __ubsan_ignore_float_divide_by_zero__ {
+  return a / static_cast<double>(b);
+}
+
+/// Arithmetic with ints
+
+inline C10_HOST_DEVICE Half operator+(Half a, int b) {
+  return a + static_cast<Half>(b);
+}
+inline C10_HOST_DEVICE Half operator-(Half a, int b) {
+  return a - static_cast<Half>(b);
+}
+inline C10_HOST_DEVICE Half operator*(Half a, int b) {
+  return a * static_cast<Half>(b);
+}
+inline C10_HOST_DEVICE Half operator/(Half a, int b) {
+  return a / static_cast<Half>(b);
+}
+
+inline C10_HOST_DEVICE Half operator+(int a, Half b) {
+  return static_cast<Half>(a) + b;
+}
+inline C10_HOST_DEVICE Half operator-(int a, Half b) {
+  return static_cast<Half>(a) - b;
+}
+inline C10_HOST_DEVICE Half operator*(int a, Half b) {
+  return static_cast<Half>(a) * b;
+}
+inline C10_HOST_DEVICE Half operator/(int a, Half b) {
+  return static_cast<Half>(a) / b;
+}
+
+//// Arithmetic with int64_t
+
+inline C10_HOST_DEVICE Half operator+(Half a, int64_t b) {
+  return a + static_cast<Half>(b);
+}
+inline C10_HOST_DEVICE Half operator-(Half a, int64_t b) {
+  return a - static_cast<Half>(b);
+}
+inline C10_HOST_DEVICE Half operator*(Half a, int64_t b) {
+  return a * static_cast<Half>(b);
+}
+inline C10_HOST_DEVICE Half operator/(Half a, int64_t b) {
+  return a / static_cast<Half>(b);
+}
+
+inline C10_HOST_DEVICE Half operator+(int64_t a, Half b) {
+  return static_cast<Half>(a) + b;
+}
+inline C10_HOST_DEVICE Half operator-(int64_t a, Half b) {
+  return static_cast<Half>(a) - b;
+}
+inline C10_HOST_DEVICE Half operator*(int64_t a, Half b) {
+  return static_cast<Half>(a) * b;
+}
+inline C10_HOST_DEVICE Half operator/(int64_t a, Half b) {
+  return static_cast<Half>(a) / b;
+}
+
+/// NOTE: we do not define comparisons directly and instead rely on the implicit
+/// conversion from c10::Half to float.
+
+} // namespace c10
+
+namespace std {
+
+template <>
+class numeric_limits<c10::Half> {
+ public:
+  static constexpr bool is_specialized = true;
+  static constexpr bool is_signed = true;
+  static constexpr bool is_integer = false;
+  static constexpr bool is_exact = false;
+  static constexpr bool has_infinity = true;
+  static constexpr bool has_quiet_NaN = true;
+  static constexpr bool has_signaling_NaN = true;
+  static constexpr auto has_denorm = numeric_limits<float>::has_denorm;
+  static constexpr auto has_denorm_loss =
+      numeric_limits<float>::has_denorm_loss;
+  static constexpr auto round_style = numeric_limits<float>::round_style;
+  static constexpr bool is_iec559 = true;
+  static constexpr bool is_bounded = true;
+  static constexpr bool is_modulo = false;
+  static constexpr int digits = 11;
+  static constexpr int digits10 = 3;
+  static constexpr int max_digits10 = 5;
+  static constexpr int radix = 2;
+  static constexpr int min_exponent = -13;
+  static constexpr int min_exponent10 = -4;
+  static constexpr int max_exponent = 16;
+  static constexpr int max_exponent10 = 4;
+  static constexpr auto traps = numeric_limits<float>::traps;
+  static constexpr auto tinyness_before =
+      numeric_limits<float>::tinyness_before;
+  static constexpr c10::Half min() {
+    return c10::Half(0x0400, c10::Half::from_bits());
+  }
+  static constexpr c10::Half lowest() {
+    return c10::Half(0xFBFF, c10::Half::from_bits());
+  }
+  static constexpr c10::Half max() {
+    return c10::Half(0x7BFF, c10::Half::from_bits());
+  }
+  static constexpr c10::Half epsilon() {
+    return c10::Half(0x1400, c10::Half::from_bits());
+  }
+  static constexpr c10::Half round_error() {
+    return c10::Half(0x3800, c10::Half::from_bits());
+  }
+  static constexpr c10::Half infinity() {
+    return c10::Half(0x7C00, c10::Half::from_bits());
+  }
+  static constexpr c10::Half quiet_NaN() {
+    return c10::Half(0x7E00, c10::Half::from_bits());
+  }
+  static constexpr c10::Half signaling_NaN() {
+    return c10::Half(0x7D00, c10::Half::from_bits());
+  }
+  static constexpr c10::Half denorm_min() {
+    return c10::Half(0x0001, c10::Half::from_bits());
+  }
+};
+
+} // namespace std
+
+C10_CLANG_DIAGNOSTIC_POP()

videochat2/lib/python3.10/site-packages/torch/include/c10/util/MathConstants.h

@@ -0,0 +1,142 @@
+#pragma once
+
+#include <c10/macros/Macros.h>
+#include <c10/util/BFloat16.h>
+#include <c10/util/Half.h>
+
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-float-conversion")
+#endif
+
+namespace c10 {
+// TODO: Replace me with inline constexpr variable when C++17 becomes available
+namespace detail {
+template <typename T>
+C10_HOST_DEVICE inline constexpr T e() {
+  return static_cast<T>(2.718281828459045235360287471352662);
+}
+
+template <typename T>
+C10_HOST_DEVICE inline constexpr T euler() {
+  return static_cast<T>(0.577215664901532860606512090082402);
+}
+
+template <typename T>
+C10_HOST_DEVICE inline constexpr T frac_1_pi() {
+  return static_cast<T>(0.318309886183790671537767526745028);
+}
+
+template <typename T>
+C10_HOST_DEVICE inline constexpr T frac_1_sqrt_pi() {
+  return static_cast<T>(0.564189583547756286948079451560772);
+}
+
+template <typename T>
+C10_HOST_DEVICE inline constexpr T frac_sqrt_2() {
+  return static_cast<T>(0.707106781186547524400844362104849);
+}
+
+template <typename T>
+C10_HOST_DEVICE inline constexpr T frac_sqrt_3() {
+  return static_cast<T>(0.577350269189625764509148780501957);
+}
+
+template <typename T>
+C10_HOST_DEVICE inline constexpr T golden_ratio() {
+  return static_cast<T>(1.618033988749894848204586834365638);
+}
+
+template <typename T>
+C10_HOST_DEVICE inline constexpr T ln_10() {
+  return static_cast<T>(2.302585092994045684017991454684364);
+}
+
+template <typename T>
+C10_HOST_DEVICE inline constexpr T ln_2() {
+  return static_cast<T>(0.693147180559945309417232121458176);
+}
+
+template <typename T>
+C10_HOST_DEVICE inline constexpr T log_10_e() {
+  return static_cast<T>(0.434294481903251827651128918916605);
+}
+
+template <typename T>
+C10_HOST_DEVICE inline constexpr T log_2_e() {
+  return static_cast<T>(1.442695040888963407359924681001892);
+}
+
+template <typename T>
+C10_HOST_DEVICE inline constexpr T pi() {
+  return static_cast<T>(3.141592653589793238462643383279502);
+}
+
+template <typename T>
+C10_HOST_DEVICE inline constexpr T sqrt_2() {
+  return static_cast<T>(1.414213562373095048801688724209698);
+}
+
+template <typename T>
+C10_HOST_DEVICE inline constexpr T sqrt_3() {
+  return static_cast<T>(1.732050807568877293527446341505872);
+}
+
+template <>
+C10_HOST_DEVICE inline constexpr BFloat16 pi<BFloat16>() {
+  // According to
+  // https://en.wikipedia.org/wiki/Bfloat16_floating-point_format#Special_values
+  // pi is encoded as 4049
+  return BFloat16(0x4049, BFloat16::from_bits());
+}
+
+template <>
+C10_HOST_DEVICE inline constexpr Half pi<Half>() {
+  return Half(0x4248, Half::from_bits());
+}
+} // namespace detail
+
+template <typename T>
+constexpr T e = c10::detail::e<T>();
+
+template <typename T>
+constexpr T euler = c10::detail::euler<T>();
+
+template <typename T>
+constexpr T frac_1_pi = c10::detail::frac_1_pi<T>();
+
+template <typename T>
+constexpr T frac_1_sqrt_pi = c10::detail::frac_1_sqrt_pi<T>();
+
+template <typename T>
+constexpr T frac_sqrt_2 = c10::detail::frac_sqrt_2<T>();
+
+template <typename T>
+constexpr T frac_sqrt_3 = c10::detail::frac_sqrt_3<T>();
+
+template <typename T>
+constexpr T golden_ratio = c10::detail::golden_ratio<T>();
+
+template <typename T>
+constexpr T ln_10 = c10::detail::ln_10<T>();
+
+template <typename T>
+constexpr T ln_2 = c10::detail::ln_2<T>();
+
+template <typename T>
+constexpr T log_10_e = c10::detail::log_10_e<T>();
+
+template <typename T>
+constexpr T log_2_e = c10::detail::log_2_e<T>();
+
+template <typename T>
+constexpr T pi = c10::detail::pi<T>();
+
+template <typename T>
+constexpr T sqrt_2 = c10::detail::sqrt_2<T>();
+
+template <typename T>
+constexpr T sqrt_3 = c10::detail::sqrt_3<T>();
+} // namespace c10
+
+C10_CLANG_DIAGNOSTIC_POP()

videochat2/lib/python3.10/site-packages/torch/include/c10/util/MaybeOwned.h

@@ -0,0 +1,237 @@
+#pragma once
+
+#include <c10/macros/Macros.h>
+#include <c10/util/Exception.h>
+
+#include <memory>
+#include <type_traits>
+#include <utility>
+
+namespace c10 {
+
+/// MaybeOwnedTraits<T> describes how to borrow from T.  Here is how we
+/// can implement borrowing from an arbitrary type T using a raw
+/// pointer to const:
+template <typename T>
+struct MaybeOwnedTraitsGenericImpl {
+  using owned_type = T;
+  using borrow_type = const T*;
+
+  static borrow_type createBorrow(const owned_type& from) {
+    return &from;
+  }
+
+  static void assignBorrow(borrow_type& lhs, borrow_type rhs) {
+    lhs = rhs;
+  }
+
+  static void destroyBorrow(borrow_type& /*toDestroy*/) {}
+
+  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 borrow != nullptr;
+  }
+};
+
+/// It is possible to eliminate the extra layer of indirection for
+/// borrows for some types that we control. For examples, see
+/// intrusive_ptr.h and TensorBody.h.
+
+template <typename T>
+struct MaybeOwnedTraits;
+
+// Explicitly enable MaybeOwned<shared_ptr<T>>, rather than allowing
+// MaybeOwned to be used for any type right away.
+template <typename T>
+struct MaybeOwnedTraits<std::shared_ptr<T>>
+    : public MaybeOwnedTraitsGenericImpl<std::shared_ptr<T>> {};
+
+/// A smart pointer around either a borrowed or owned T. When
+/// constructed with borrowed(), the caller MUST ensure that the
+/// borrowed-from argument outlives this MaybeOwned<T>. Compare to
+/// Rust's std::borrow::Cow
+/// (https://doc.rust-lang.org/std/borrow/enum.Cow.html), but note
+/// that it is probably not suitable for general use because C++ has
+/// no borrow checking. Included here to support
+/// Tensor::expect_contiguous.
+template <typename T>
+class MaybeOwned final {
+  using borrow_type = typename MaybeOwnedTraits<T>::borrow_type;
+  using owned_type = typename MaybeOwnedTraits<T>::owned_type;
+
+  bool isBorrowed_;
+  union {
+    borrow_type borrow_;
+    owned_type own_;
+  };
+
+  /// Don't use this; use borrowed() instead.
+  explicit MaybeOwned(const owned_type& t)
+      : isBorrowed_(true), borrow_(MaybeOwnedTraits<T>::createBorrow(t)) {}
+
+  /// Don't use this; use owned() instead.
+  explicit MaybeOwned(T&& t) noexcept(std::is_nothrow_move_constructible_v<T>)
+      : isBorrowed_(false), own_(std::move(t)) {}
+
+  /// Don't use this; use owned() instead.
+  template <class... Args>
+  explicit MaybeOwned(std::in_place_t, Args&&... args)
+      : isBorrowed_(false), own_(std::forward<Args>(args)...) {}
+
+ public:
+  explicit MaybeOwned() : isBorrowed_(true), borrow_() {}
+
+  // Copying a borrow yields another borrow of the original, as with a
+  // T*. Copying an owned T yields another owned T for safety: no
+  // chains of borrowing by default! (Note you could get that behavior
+  // with MaybeOwned<T>::borrowed(*rhs) if you wanted it.)
+  MaybeOwned(const MaybeOwned& rhs) : isBorrowed_(rhs.isBorrowed_) {
+    if (C10_LIKELY(rhs.isBorrowed_)) {
+      MaybeOwnedTraits<T>::assignBorrow(borrow_, rhs.borrow_);
+    } else {
+      new (&own_) T(rhs.own_);
+    }
+  }
+
+  MaybeOwned& operator=(const MaybeOwned& rhs) {
+    if (this == &rhs) {
+      return *this;
+    }
+    if (C10_UNLIKELY(!isBorrowed_)) {
+      if (rhs.isBorrowed_) {
+        own_.~T();
+        MaybeOwnedTraits<T>::assignBorrow(borrow_, rhs.borrow_);
+        isBorrowed_ = true;
+      } else {
+        own_ = rhs.own_;
+      }
+    } else {
+      if (C10_LIKELY(rhs.isBorrowed_)) {
+        MaybeOwnedTraits<T>::assignBorrow(borrow_, rhs.borrow_);
+      } else {
+        MaybeOwnedTraits<T>::destroyBorrow(borrow_);
+        new (&own_) T(rhs.own_);
+        isBorrowed_ = false;
+      }
+    }
+    TORCH_INTERNAL_ASSERT_DEBUG_ONLY(isBorrowed_ == rhs.isBorrowed_);
+    return *this;
+  }
+
+  MaybeOwned(MaybeOwned&& rhs) noexcept(
+      // NOLINTNEXTLINE(*-noexcept-move-*)
+      std::is_nothrow_move_constructible_v<T> &&
+      std::is_nothrow_move_assignable_v<borrow_type>)
+      : isBorrowed_(rhs.isBorrowed_) {
+    if (C10_LIKELY(rhs.isBorrowed_)) {
+      MaybeOwnedTraits<T>::assignBorrow(borrow_, rhs.borrow_);
+    } else {
+      new (&own_) T(std::move(rhs.own_));
+    }
+  }
+
+  MaybeOwned& operator=(MaybeOwned&& rhs) noexcept(
+      std::is_nothrow_move_assignable_v<T> &&
+      std::is_nothrow_move_assignable_v<borrow_type> &&
+      std::is_nothrow_move_constructible_v<T> &&
+      // NOLINTNEXTLINE(*-noexcept-move-*)
+      std::is_nothrow_destructible_v<T> &&
+      std::is_nothrow_destructible_v<borrow_type>) {
+    if (this == &rhs) {
+      return *this;
+    }
+    if (C10_UNLIKELY(!isBorrowed_)) {
+      if (rhs.isBorrowed_) {
+        own_.~T();
+        MaybeOwnedTraits<T>::assignBorrow(borrow_, rhs.borrow_);
+        isBorrowed_ = true;
+      } else {
+        own_ = std::move(rhs.own_);
+      }
+    } else {
+      if (C10_LIKELY(rhs.isBorrowed_)) {
+        MaybeOwnedTraits<T>::assignBorrow(borrow_, rhs.borrow_);
+      } else {
+        MaybeOwnedTraits<T>::destroyBorrow(borrow_);
+        new (&own_) T(std::move(rhs.own_));
+        isBorrowed_ = false;
+      }
+    }
+    return *this;
+  }
+
+  static MaybeOwned borrowed(const T& t) {
+    return MaybeOwned(t);
+  }
+
+  static MaybeOwned owned(T&& t) noexcept(
+      std::is_nothrow_move_constructible_v<T>) {
+    return MaybeOwned(std::move(t));
+  }
+
+  template <class... Args>
+  static MaybeOwned owned(std::in_place_t, Args&&... args) {
+    return MaybeOwned(std::in_place, std::forward<Args>(args)...);
+  }
+
+  ~MaybeOwned() noexcept(
+      // NOLINTNEXTLINE(*-noexcept-destructor)
+      std::is_nothrow_destructible_v<T> &&
+      std::is_nothrow_destructible_v<borrow_type>) {
+    if (C10_UNLIKELY(!isBorrowed_)) {
+      own_.~T();
+    } else {
+      MaybeOwnedTraits<T>::destroyBorrow(borrow_);
+    }
+  }
+
+  // This is an implementation detail!  You should know what you're doing
+  // if you are testing this.  If you just want to guarantee ownership move
+  // this into a T
+  bool unsafeIsBorrowed() const {
+    return isBorrowed_;
+  }
+
+  const T& operator*() const& {
+    if (isBorrowed_) {
+      TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+          MaybeOwnedTraits<T>::debugBorrowIsValid(borrow_));
+    }
+    return C10_LIKELY(isBorrowed_)
+        ? MaybeOwnedTraits<T>::referenceFromBorrow(borrow_)
+        : own_;
+  }
+
+  const T* operator->() const {
+    if (isBorrowed_) {
+      TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+          MaybeOwnedTraits<T>::debugBorrowIsValid(borrow_));
+    }
+    return C10_LIKELY(isBorrowed_)
+        ? MaybeOwnedTraits<T>::pointerFromBorrow(borrow_)
+        : &own_;
+  }
+
+  // If borrowed, copy the underlying T. If owned, move from
+  // it. borrowed/owned state remains the same, and either we
+  // reference the same borrow as before or we are an owned moved-from
+  // T.
+  T operator*() && {
+    if (isBorrowed_) {
+      TORCH_INTERNAL_ASSERT_DEBUG_ONLY(
+          MaybeOwnedTraits<T>::debugBorrowIsValid(borrow_));
+      return MaybeOwnedTraits<T>::referenceFromBorrow(borrow_);
+    } else {
+      return std::move(own_);
+    }
+  }
+};
+
+} // namespace c10

videochat2/lib/python3.10/site-packages/torch/include/c10/util/Metaprogramming.h

@@ -0,0 +1,224 @@
+#pragma once
+
+#include <c10/util/TypeList.h>
+#include <type_traits>
+
+namespace c10::guts {
+
+/**
+ * Access information about result type or arguments from a function type.
+ * Example:
+ * using A = function_traits<int (float, double)>::return_type // A == int
+ * using A = function_traits<int (float, double)>::parameter_types::tuple_type
+ * // A == tuple<float, double>
+ */
+template <class Func>
+struct function_traits {
+  static_assert(
+      !std::is_same_v<Func, Func>,
+      "In function_traits<Func>, Func must be a plain function type.");
+};
+template <class Result, class... Args>
+struct function_traits<Result(Args...)> {
+  using func_type = Result(Args...);
+  using return_type = Result;
+  using parameter_types = typelist::typelist<Args...>;
+  static constexpr auto number_of_parameters = sizeof...(Args);
+};
+
+/**
+ * infer_function_traits: creates a `function_traits` type for a simple
+ * function (pointer) or functor (lambda/struct). Currently does not support
+ * class methods.
+ */
+
+template <typename Functor>
+struct infer_function_traits {
+  using type = function_traits<
+      c10::guts::detail::strip_class_t<decltype(&Functor::operator())>>;
+};
+
+template <typename Result, typename... Args>
+struct infer_function_traits<Result (*)(Args...)> {
+  using type = function_traits<Result(Args...)>;
+};
+
+template <typename Result, typename... Args>
+struct infer_function_traits<Result(Args...)> {
+  using type = function_traits<Result(Args...)>;
+};
+
+template <typename T>
+using infer_function_traits_t = typename infer_function_traits<T>::type;
+
+/**
+ * make_function_traits: creates a `function_traits` type given a Return type
+ * and a typelist of Argument types
+ *
+ * Example:
+ * bool f(int, int);
+ *
+ * infer_function_traits_t<f> == make_function_traits_t<bool,
+ * typelist::typelist<int, int>>
+ */
+template <typename Result, typename ArgList>
+struct make_function_traits {
+  static_assert(
+      false_t<ArgList>::value,
+      "In guts::make_function_traits<Result, TypeList>, the ArgList argument must be typelist<...>.");
+};
+
+template <typename Result, typename... Args>
+struct make_function_traits<Result, typelist::typelist<Args...>> {
+  using type = function_traits<Result(Args...)>;
+};
+
+template <typename Result, typename ArgList>
+using make_function_traits_t =
+    typename make_function_traits<Result, ArgList>::type;
+
+/**
+ * make_offset_index_sequence<Start, N>
+ * Like make_index_sequence<N>, but starting from Start instead of 0.
+ *
+ * Example:
+ *  make_offset_index_sequence<10, 3> == std::index_sequence<10, 11, 12>
+ */
+template <size_t Start, size_t N, size_t... Is>
+struct make_offset_index_sequence_impl
+    : make_offset_index_sequence_impl<Start, N - 1, Start + N - 1, Is...> {
+  static_assert(
+      static_cast<int>(Start) >= 0,
+      "make_offset_index_sequence: Start < 0");
+  static_assert(static_cast<int>(N) >= 0, "make_offset_index_sequence: N < 0");
+};
+
+template <size_t Start, size_t... Is>
+struct make_offset_index_sequence_impl<Start, 0, Is...> {
+  typedef std::index_sequence<Is...> type;
+};
+
+template <size_t Start, size_t N>
+using make_offset_index_sequence =
+    typename make_offset_index_sequence_impl<Start, N>::type;
+
+/**
+ * Use tuple_elements to extract a position-indexed subset of elements
+ * from the argument tuple into a result tuple.
+ *
+ * Example:
+ *  std::tuple<int, const char*, double> t = std::make_tuple(0, "HEY", 2.0);
+ *  std::tuple<int, double> result = tuple_elements(t, std::index_sequence<0,
+ * 2>());
+ */
+template <class Tuple, size_t... Is>
+constexpr auto tuple_elements(Tuple t, std::index_sequence<Is...>) {
+  return std::tuple<std::tuple_element_t<Is, Tuple>...>(std::get<Is>(t)...);
+}
+
+/**
+ * Use tuple_take to extract the first or last n elements from the argument
+ * tuple into a result tuple.
+ *
+ * Example:
+ *  std::tuple<int, const char*, double> t = std::make_tuple(0, "HEY", 2.0);
+ *  std::tuple<int, const char*> first_two = tuple_take<decltype(t), 2>(t);
+ *  std::tuple<const char*, double> last_two = tuple_take<decltype(t), -2>(t);
+ */
+template <class Tuple, int N, class Enable = void>
+struct TupleTake {};
+
+template <class Tuple, int N>
+struct TupleTake<Tuple, N, std::enable_if_t<N >= 0, void>> {
+  static auto call(Tuple t) {
+    constexpr size_t size = std::tuple_size<Tuple>();
+    static_assert(N <= size, "tuple_take: N > size");
+    return tuple_elements(t, std::make_index_sequence<N>{});
+  }
+};
+
+template <class Tuple, int N>
+    struct TupleTake < Tuple,
+    N, std::enable_if_t<N<0, void>> {
+  static auto call(Tuple t) {
+    constexpr size_t size = std::tuple_size<Tuple>();
+    static_assert(-N <= size, "tuple_take: -N > size");
+    return tuple_elements(t, make_offset_index_sequence<size + N, -N>{});
+  }
+};
+
+template <class Tuple, int N>
+auto tuple_take(Tuple t) {
+  return TupleTake<Tuple, N>::call(t);
+}
+
+/**
+ * Use tuple_slice to extract a contiguous subtuple from the argument.
+ *
+ * Example:
+ *  std::tuple<int, const char*, double, bool> t = std::make_tuple(0,
+ * "HEY", 2.0, false); std::tuple<int, const char*> middle_two =
+ * tuple_slice<decltype(t), 1, 2>(t);
+ */
+template <class Tuple, size_t Start, size_t N>
+constexpr auto tuple_slice(Tuple t) {
+  constexpr size_t size = std::tuple_size<Tuple>();
+  static_assert(Start + N <= size, "tuple_slice: Start + N > size");
+  return tuple_elements(t, make_offset_index_sequence<Start, N>{});
+}
+
+/**
+ * Use tuple_map to run a mapping function over a tuple to get a new tuple.
+ *
+ * Example 1:
+ *   auto result = tuple_map(std::tuple<int32_t, int32_t, int32_t>(3, 4, 5), []
+ * (int32_t a) -> int16_t {return a+1;});
+ *   // result == std::tuple<int16_t, int16_t, int16_t>(4, 5, 6)
+ *
+ * Example 2:
+ *   struct Mapper {
+ *     std::string operator()(int32_t a) const {
+ *       return std::to_string(a);
+ *     }
+ *     int64_t operator()(const std::string& a) const {
+ *        return atoi(a.c_str());
+ *     }
+ *   };
+ *   auto result = tuple_map(std::tuple<int32_t, std::string>(3, "4"),
+ * Mapper());
+ *   // result == std::tuple<std::string, int64_t>("3", 4)
+ *
+ * Example 3:
+ *   struct A final {
+ *    int32_t func() {
+ *      return 5;
+ *    }
+ *  };
+ *  struct B final {
+ *    std::string func() {
+ *      return "5";
+ *    }
+ *  };
+ *  auto result = tuple_map(std::make_tuple(A(), B()), [] (auto a) { return
+ * a.func(); });
+ *  // result == std::tuple<int32_t, std::string>(5, "5");
+ */
+namespace detail {
+template <class Mapper, class... Args, size_t... Indices>
+auto tuple_map(
+    // NOLINTNEXTLINE(cppcoreguidelines-rvalue-reference-param-not-moved)
+    std::tuple<Args...>&& tuple,
+    const Mapper& mapper,
+    std::index_sequence<Indices...>) {
+  return std::tuple<decltype(mapper(std::forward<Args>(std::get<Indices>(
+      tuple))))...>(mapper(std::forward<Args>(std::get<Indices>(tuple)))...);
+}
+} // namespace detail
+
+template <class Mapper, class... Args>
+auto tuple_map(std::tuple<Args...>&& tuple, const Mapper& mapper) {
+  return detail::tuple_map(
+      std::move(tuple), mapper, std::index_sequence_for<Args...>());
+}
+
+} // namespace c10::guts

videochat2/lib/python3.10/site-packages/torch/include/c10/util/Optional.h

@@ -0,0 +1,48 @@
+#ifndef C10_UTIL_OPTIONAL_H_
+#define C10_UTIL_OPTIONAL_H_
+
+#include <optional>
+#include <type_traits>
+
+// Macros.h is not needed, but it does namespace shenanigans that lots
+// of downstream code seems to rely on. Feel free to remove it and fix
+// up builds.
+
+namespace c10 {
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using std::bad_optional_access;
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using std::make_optional;
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using std::nullopt;
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using std::nullopt_t;
+// NOLINTNEXTLINE(misc-unused-using-decls)
+using std::optional;
+
+namespace detail_ {
+// the call to convert<A>(b) has return type A and converts b to type A iff b
+// decltype(b) is implicitly convertible to A
+template <class U>
+constexpr U convert(U v) {
+  return v;
+}
+} // namespace detail_
+template <class T, class F>
+constexpr T value_or_else(const std::optional<T>& v, F&& func) {
+  static_assert(
+      std::is_convertible_v<typename std::invoke_result_t<F>, T>,
+      "func parameters must be a callable that returns a type convertible to the value stored in the optional");
+  return v.has_value() ? *v : detail_::convert<T>(std::forward<F>(func)());
+}
+
+template <class T, class F>
+constexpr T value_or_else(std::optional<T>&& v, F&& func) {
+  static_assert(
+      std::is_convertible_v<typename std::invoke_result_t<F>, T>,
+      "func parameters must be a callable that returns a type convertible to the value stored in the optional");
+  return v.has_value() ? constexpr_move(std::move(v).contained_val())
+                       : detail_::convert<T>(std::forward<F>(func)());
+}
+} // namespace c10
+#endif // C10_UTIL_OPTIONAL_H_

videochat2/lib/python3.10/site-packages/torch/include/c10/util/OptionalArrayRef.h

@@ -0,0 +1,236 @@
+// This file defines OptionalArrayRef<T>, a class that has almost the same
+// exact functionality as std::optional<ArrayRef<T>>, except that its
+// converting constructor fixes a dangling pointer issue.
+//
+// The implicit converting constructor of both std::optional<ArrayRef<T>> and
+// std::optional<ArrayRef<T>> can cause the underlying ArrayRef<T> to store
+// a dangling pointer. OptionalArrayRef<T> prevents this by wrapping
+// a std::optional<ArrayRef<T>> and fixing the constructor implementation.
+//
+// See https://github.com/pytorch/pytorch/issues/63645 for more on this.
+
+#pragma once
+
+#include <c10/util/ArrayRef.h>
+#include <cstdint>
+#include <initializer_list>
+#include <optional>
+#include <type_traits>
+#include <utility>
+
+namespace c10 {
+
+template <typename T>
+class OptionalArrayRef final {
+ public:
+  // Constructors
+
+  constexpr OptionalArrayRef() noexcept = default;
+
+  constexpr OptionalArrayRef(std::nullopt_t) noexcept {}
+
+  OptionalArrayRef(const OptionalArrayRef& other) = default;
+
+  OptionalArrayRef(OptionalArrayRef&& other) noexcept = default;
+
+  constexpr OptionalArrayRef(const std::optional<ArrayRef<T>>& other) noexcept
+      : wrapped_opt_array_ref(other) {}
+
+  constexpr OptionalArrayRef(std::optional<ArrayRef<T>>&& other) noexcept
+      : wrapped_opt_array_ref(std::move(other)) {}
+
+  constexpr OptionalArrayRef(const T& value) noexcept
+      : wrapped_opt_array_ref(value) {}
+
+  template <
+      typename U = ArrayRef<T>,
+      std::enable_if_t<
+          !std::is_same_v<std::decay_t<U>, OptionalArrayRef> &&
+              !std::is_same_v<std::decay_t<U>, std::in_place_t> &&
+              std::is_constructible_v<ArrayRef<T>, U&&> &&
+              std::is_convertible_v<U&&, ArrayRef<T>> &&
+              !std::is_convertible_v<U&&, T>,
+          bool> = false>
+  constexpr OptionalArrayRef(U&& value) noexcept(
+      std::is_nothrow_constructible_v<ArrayRef<T>, U&&>)
+      : wrapped_opt_array_ref(std::forward<U>(value)) {}
+
+  template <
+      typename U = ArrayRef<T>,
+      std::enable_if_t<
+          !std::is_same_v<std::decay_t<U>, OptionalArrayRef> &&
+              !std::is_same_v<std::decay_t<U>, std::in_place_t> &&
+              std::is_constructible_v<ArrayRef<T>, U&&> &&
+              !std::is_convertible_v<U&&, ArrayRef<T>>,
+          bool> = false>
+  constexpr explicit OptionalArrayRef(U&& value) noexcept(
+      std::is_nothrow_constructible_v<ArrayRef<T>, U&&>)
+      : wrapped_opt_array_ref(std::forward<U>(value)) {}
+
+  template <typename... Args>
+  constexpr explicit OptionalArrayRef(
+      std::in_place_t ip,
+      Args&&... args) noexcept
+      : wrapped_opt_array_ref(ip, std::forward<Args>(args)...) {}
+
+  template <typename U, typename... Args>
+  constexpr explicit OptionalArrayRef(
+      std::in_place_t ip,
+      std::initializer_list<U> il,
+      Args&&... args)
+      : wrapped_opt_array_ref(ip, il, std::forward<Args>(args)...) {}
+
+  constexpr OptionalArrayRef(const std::initializer_list<T>& Vec)
+      : wrapped_opt_array_ref(ArrayRef<T>(Vec)) {}
+
+  // Destructor
+
+  ~OptionalArrayRef() = default;
+
+  // Assignment
+
+  constexpr OptionalArrayRef& operator=(std::nullopt_t) noexcept {
+    wrapped_opt_array_ref = std::nullopt;
+    return *this;
+  }
+
+  OptionalArrayRef& operator=(const OptionalArrayRef& other) = default;
+
+  OptionalArrayRef& operator=(OptionalArrayRef&& other) noexcept = default;
+
+  constexpr OptionalArrayRef& operator=(
+      const std::optional<ArrayRef<T>>& other) noexcept {
+    wrapped_opt_array_ref = other;
+    return *this;
+  }
+
+  constexpr OptionalArrayRef& operator=(
+      std::optional<ArrayRef<T>>&& other) noexcept {
+    wrapped_opt_array_ref = std::move(other);
+    return *this;
+  }
+
+  template <
+      typename U = ArrayRef<T>,
+      typename = std::enable_if_t<
+          !std::is_same_v<std::decay_t<U>, OptionalArrayRef> &&
+          std::is_constructible_v<ArrayRef<T>, U&&> &&
+          std::is_assignable_v<ArrayRef<T>&, U&&>>>
+  constexpr OptionalArrayRef& operator=(U&& value) noexcept(
+      std::is_nothrow_constructible_v<ArrayRef<T>, U&&> &&
+      std::is_nothrow_assignable_v<ArrayRef<T>&, U&&>) {
+    wrapped_opt_array_ref = std::forward<U>(value);
+    return *this;
+  }
+
+  // Observers
+
+  constexpr ArrayRef<T>* operator->() noexcept {
+    return &wrapped_opt_array_ref.value();
+  }
+
+  constexpr const ArrayRef<T>* operator->() const noexcept {
+    return &wrapped_opt_array_ref.value();
+  }
+
+  constexpr ArrayRef<T>& operator*() & noexcept {
+    return wrapped_opt_array_ref.value();
+  }
+
+  constexpr const ArrayRef<T>& operator*() const& noexcept {
+    return wrapped_opt_array_ref.value();
+  }
+
+  constexpr ArrayRef<T>&& operator*() && noexcept {
+    return std::move(wrapped_opt_array_ref.value());
+  }
+
+  constexpr const ArrayRef<T>&& operator*() const&& noexcept {
+    return std::move(wrapped_opt_array_ref.value());
+  }
+
+  constexpr explicit operator bool() const noexcept {
+    return wrapped_opt_array_ref.has_value();
+  }
+
+  constexpr bool has_value() const noexcept {
+    return wrapped_opt_array_ref.has_value();
+  }
+
+  constexpr ArrayRef<T>& value() & {
+    return wrapped_opt_array_ref.value();
+  }
+
+  constexpr const ArrayRef<T>& value() const& {
+    return wrapped_opt_array_ref.value();
+  }
+
+  constexpr ArrayRef<T>&& value() && {
+    return std::move(wrapped_opt_array_ref.value());
+  }
+
+  constexpr const ArrayRef<T>&& value() const&& {
+    return std::move(wrapped_opt_array_ref.value());
+  }
+
+  template <typename U>
+  constexpr std::
+      enable_if_t<std::is_convertible_v<U&&, ArrayRef<T>>, ArrayRef<T>>
+      value_or(U&& default_value) const& {
+    return wrapped_opt_array_ref.value_or(std::forward<U>(default_value));
+  }
+
+  template <typename U>
+  constexpr std::
+      enable_if_t<std::is_convertible_v<U&&, ArrayRef<T>>, ArrayRef<T>>
+      value_or(U&& default_value) && {
+    return wrapped_opt_array_ref.value_or(std::forward<U>(default_value));
+  }
+
+  // Modifiers
+
+  constexpr void swap(OptionalArrayRef& other) noexcept {
+    std::swap(wrapped_opt_array_ref, other.wrapped_opt_array_ref);
+  }
+
+  constexpr void reset() noexcept {
+    wrapped_opt_array_ref.reset();
+  }
+
+  template <typename... Args>
+  constexpr std::
+      enable_if_t<std::is_constructible_v<ArrayRef<T>, Args&&...>, ArrayRef<T>&>
+      emplace(Args&&... args) noexcept(
+          std::is_nothrow_constructible_v<ArrayRef<T>, Args&&...>) {
+    return wrapped_opt_array_ref.emplace(std::forward<Args>(args)...);
+  }
+
+  template <typename U, typename... Args>
+  constexpr ArrayRef<T>& emplace(
+      std::initializer_list<U> il,
+      Args&&... args) noexcept {
+    return wrapped_opt_array_ref.emplace(il, std::forward<Args>(args)...);
+  }
+
+ private:
+  std::optional<ArrayRef<T>> wrapped_opt_array_ref;
+};
+
+using OptionalIntArrayRef = OptionalArrayRef<int64_t>;
+
+inline bool operator==(
+    const OptionalIntArrayRef& a1,
+    const IntArrayRef& other) {
+  if (!a1.has_value()) {
+    return false;
+  }
+  return a1.value() == other;
+}
+
+inline bool operator==(
+    const c10::IntArrayRef& a1,
+    const c10::OptionalIntArrayRef& a2) {
+  return a2 == a1;
+}
+
+} // namespace c10

videochat2/lib/python3.10/site-packages/torch/include/c10/util/SmallVector.h

@@ -0,0 +1,1467 @@
+//===- llvm/ADT/SmallVector.h - 'Normally small' vectors --------*- C++ -*-===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+//
+// This file defines the SmallVector class.
+//
+//===----------------------------------------------------------------------===//
+
+// ATen: modified from llvm::SmallVector.
+// used std::is_trivially_{copy,move}_constructible
+// replaced iterator_range constructor with inline Container&& constructor
+// replaced LLVM_NODISCARD, LLVM_LIKELY, and LLVM_UNLIKELY with c10 equivalents
+// removed LLVM_GSL_OWNER
+// added SmallVector::at
+// added operator<< for std::ostream
+// added C10_API to export SmallVectorBase
+
+#pragma once
+
+#include <c10/macros/Macros.h>
+#include <c10/util/AlignOf.h>
+
+#include <algorithm>
+#include <cassert>
+#include <cstddef>
+#include <cstdlib>
+#include <cstring>
+#include <functional>
+#include <initializer_list>
+#include <iterator>
+#include <limits>
+#include <memory>
+#include <ostream>
+#include <type_traits>
+#include <utility>
+
+namespace c10 {
+
+/// This is all the stuff common to all SmallVectors.
+///
+/// The template parameter specifies the type which should be used to hold the
+/// Size and Capacity of the SmallVector, so it can be adjusted.
+/// Using 32 bit size is desirable to shrink the size of the SmallVector.
+/// Using 64 bit size is desirable for cases like SmallVector<char>, where a
+/// 32 bit size would limit the vector to ~4GB. SmallVectors are used for
+/// buffering bitcode output - which can exceed 4GB.
+template <class Size_T>
+class C10_API SmallVectorBase {
+ protected:
+  void* BeginX;
+  Size_T Size = 0, Capacity;
+
+  /// The maximum value of the Size_T used.
+  static constexpr size_t SizeTypeMax() {
+    return std::numeric_limits<Size_T>::max();
+  }
+
+  SmallVectorBase(void* FirstEl, size_t TotalCapacity)
+      : BeginX(FirstEl), Capacity(TotalCapacity) {}
+
+  /// This is a helper for \a grow() that's out of line to reduce code
+  /// duplication.  This function will report a fatal error if it can't grow at
+  /// least to \p MinSize.
+  void* mallocForGrow(size_t MinSize, size_t TSize, size_t& NewCapacity);
+
+  /// This is an implementation of the grow() method which only works
+  /// on POD-like data types and is out of line to reduce code duplication.
+  /// This function will report a fatal error if it cannot increase capacity.
+  void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize);
+
+ public:
+  SmallVectorBase() = delete;
+  size_t size() const {
+    return Size;
+  }
+  size_t capacity() const {
+    return Capacity;
+  }
+
+  C10_NODISCARD bool empty() const {
+    return !Size;
+  }
+
+  /// Set the array size to \p N, which the current array must have enough
+  /// capacity for.
+  ///
+  /// This does not construct or destroy any elements in the vector.
+  ///
+  /// Clients can use this in conjunction with capacity() to write past the end
+  /// of the buffer when they know that more elements are available, and only
+  /// update the size later. This avoids the cost of value initializing elements
+  /// which will only be overwritten.
+  void set_size(size_t N) {
+    assert(N <= capacity());
+    Size = N;
+  }
+};
+
+template <class T>
+using SmallVectorSizeType =
+    std::conditional_t<sizeof(T) < 4 && sizeof(void*) >= 8, uint64_t, uint32_t>;
+
+/// Figure out the offset of the first element.
+template <class T, typename = void>
+struct SmallVectorAlignmentAndSize {
+  // NOLINTNEXTLINE(*c-arrays*)
+  alignas(SmallVectorBase<SmallVectorSizeType<T>>) char Base[sizeof(
+      SmallVectorBase<SmallVectorSizeType<T>>)];
+  // NOLINTNEXTLINE(*c-arrays*)
+  alignas(T) char FirstEl[sizeof(T)];
+};
+
+/// This is the part of SmallVectorTemplateBase which does not depend on whether
+/// the type T is a POD. The extra dummy template argument is used by ArrayRef
+/// to avoid unnecessarily requiring T to be complete.
+template <typename T, typename = void>
+class SmallVectorTemplateCommon
+    : public SmallVectorBase<SmallVectorSizeType<T>> {
+  using Base = SmallVectorBase<SmallVectorSizeType<T>>;
+
+  /// Find the address of the first element.  For this pointer math to be valid
+  /// with small-size of 0 for T with lots of alignment, it's important that
+  /// SmallVectorStorage is properly-aligned even for small-size of 0.
+  void* getFirstEl() const {
+    return const_cast<void*>(reinterpret_cast<const void*>(
+        reinterpret_cast<const char*>(this) +
+        offsetof(SmallVectorAlignmentAndSize<T>, FirstEl)));
+  }
+  // Space after 'FirstEl' is clobbered, do not add any instance vars after it.
+
+ protected:
+  SmallVectorTemplateCommon(size_t Size) : Base(getFirstEl(), Size) {}
+
+  void grow_pod(size_t MinSize, size_t TSize) {
+    Base::grow_pod(getFirstEl(), MinSize, TSize);
+  }
+
+  /// Return true if this is a smallvector which has not had dynamic
+  /// memory allocated for it.
+  bool isSmall() const {
+    return this->BeginX == getFirstEl();
+  }
+
+  /// Put this vector in a state of being small.
+  void resetToSmall() {
+    this->BeginX = getFirstEl();
+    this->Size = this->Capacity = 0; // FIXME: Setting Capacity to 0 is suspect.
+  }
+
+  /// Return true if V is an internal reference to the given range.
+  bool isReferenceToRange(const void* V, const void* First, const void* Last)
+      const {
+    // Use std::less to avoid UB.
+    std::less<> LessThan;
+    return !LessThan(V, First) && LessThan(V, Last);
+  }
+
+  /// Return true if V is an internal reference to this vector.
+  bool isReferenceToStorage(const void* V) const {
+    return isReferenceToRange(V, this->begin(), this->end());
+  }
+
+  /// Return true if First and Last form a valid (possibly empty) range in this
+  /// vector's storage.
+  bool isRangeInStorage(const void* First, const void* Last) const {
+    // Use std::less to avoid UB.
+    std::less<> LessThan;
+    return !LessThan(First, this->begin()) && !LessThan(Last, First) &&
+        !LessThan(this->end(), Last);
+  }
+
+  /// Return true unless Elt will be invalidated by resizing the vector to
+  /// NewSize.
+  bool isSafeToReferenceAfterResize(const void* Elt, size_t NewSize) {
+    // Past the end.
+    if (C10_LIKELY(!isReferenceToStorage(Elt)))
+      return true;
+
+    // Return false if Elt will be destroyed by shrinking.
+    if (NewSize <= this->size())
+      return Elt < this->begin() + NewSize;
+
+    // Return false if we need to grow.
+    return NewSize <= this->capacity();
+  }
+
+  /// Check whether Elt will be invalidated by resizing the vector to NewSize.
+  void assertSafeToReferenceAfterResize(const void* Elt, size_t NewSize) {
+    (void)Elt; // Suppress unused variable warning
+    (void)NewSize; // Suppress unused variable warning
+    assert(
+        isSafeToReferenceAfterResize(Elt, NewSize) &&
+        "Attempting to reference an element of the vector in an operation "
+        "that invalidates it");
+  }
+
+  /// Check whether Elt will be invalidated by increasing the size of the
+  /// vector by N.
+  void assertSafeToAdd(const void* Elt, size_t N = 1) {
+    this->assertSafeToReferenceAfterResize(Elt, this->size() + N);
+  }
+
+  /// Check whether any part of the range will be invalidated by clearing.
+  void assertSafeToReferenceAfterClear(const T* From, const T* To) {
+    if (From == To)
+      return;
+    this->assertSafeToReferenceAfterResize(From, 0);
+    this->assertSafeToReferenceAfterResize(To - 1, 0);
+  }
+  template <
+      class ItTy,
+      std::enable_if_t<!std::is_same_v<std::remove_const_t<ItTy>, T*>, bool> =
+          false>
+  void assertSafeToReferenceAfterClear(ItTy, ItTy) {}
+
+  /// Check whether any part of the range will be invalidated by growing.
+  void assertSafeToAddRange(const T* From, const T* To) {
+    if (From == To)
+      return;
+    this->assertSafeToAdd(From, To - From);
+    this->assertSafeToAdd(To - 1, To - From);
+  }
+  template <
+      class ItTy,
+      std::enable_if_t<!std::is_same_v<std::remove_const_t<ItTy>, T*>, bool> =
+          false>
+  void assertSafeToAddRange(ItTy, ItTy) {}
+
+  /// Reserve enough space to add one element, and return the updated element
+  /// pointer in case it was a reference to the storage.
+  template <class U>
+  static const T* reserveForParamAndGetAddressImpl(
+      U* This,
+      const T& Elt,
+      size_t N) {
+    size_t NewSize = This->size() + N;
+    if (C10_LIKELY(NewSize <= This->capacity()))
+      return &Elt;
+
+    bool ReferencesStorage = false;
+    int64_t Index = -1;
+    if constexpr (!U::TakesParamByValue) {
+      if (C10_UNLIKELY(This->isReferenceToStorage(&Elt))) {
+        ReferencesStorage = true;
+        Index = &Elt - This->begin();
+      }
+    }
+    This->grow(NewSize);
+    return ReferencesStorage ? This->begin() + Index : &Elt;
+  }
+
+ public:
+  using size_type = size_t;
+  using difference_type = ptrdiff_t;
+  using value_type = T;
+  using iterator = T*;
+  using const_iterator = const T*;
+
+  using const_reverse_iterator = std::reverse_iterator<const_iterator>;
+  using reverse_iterator = std::reverse_iterator<iterator>;
+
+  using reference = T&;
+  using const_reference = const T&;
+  using pointer = T*;
+  using const_pointer = const T*;
+
+  using Base::capacity;
+  using Base::empty;
+  using Base::size;
+
+  // forward iterator creation methods.
+  iterator begin() {
+    return (iterator)this->BeginX;
+  }
+  const_iterator begin() const {
+    return (const_iterator)this->BeginX;
+  }
+  iterator end() {
+    return begin() + size();
+  }
+  const_iterator end() const {
+    return begin() + size();
+  }
+
+  // reverse iterator creation methods.
+  reverse_iterator rbegin() {
+    return reverse_iterator(end());
+  }
+  const_reverse_iterator rbegin() const {
+    return const_reverse_iterator(end());
+  }
+  reverse_iterator rend() {
+    return reverse_iterator(begin());
+  }
+  const_reverse_iterator rend() const {
+    return const_reverse_iterator(begin());
+  }
+
+  size_type size_in_bytes() const {
+    return size() * sizeof(T);
+  }
+  constexpr size_type max_size() const {
+    return std::min(this->SizeTypeMax(), size_type(-1) / sizeof(T));
+  }
+
+  size_t capacity_in_bytes() const {
+    return capacity() * sizeof(T);
+  }
+
+  /// Return a pointer to the vector's buffer, even if empty().
+  pointer data() {
+    return pointer(begin());
+  }
+  /// Return a pointer to the vector's buffer, even if empty().
+  const_pointer data() const {
+    return const_pointer(begin());
+  }
+
+  // SmallVector::at is NOT from LLVM.
+  reference at(size_type idx) {
+    assert(idx < size());
+    return begin()[idx];
+  }
+  const_reference at(size_type idx) const {
+    assert(idx < size());
+    return begin()[idx];
+  }
+  reference operator[](size_type idx) {
+    assert(idx < size());
+    return begin()[idx];
+  }
+  const_reference operator[](size_type idx) const {
+    assert(idx < size());
+    return begin()[idx];
+  }
+
+  reference front() {
+    assert(!empty());
+    return begin()[0];
+  }
+  const_reference front() const {
+    assert(!empty());
+    return begin()[0];
+  }
+
+  reference back() {
+    assert(!empty());
+    return end()[-1];
+  }
+  const_reference back() const {
+    assert(!empty());
+    return end()[-1];
+  }
+};
+
+/// SmallVectorTemplateBase<TriviallyCopyable = false> - This is where we put
+/// method implementations that are designed to work with non-trivial T's.
+///
+/// We approximate is_trivially_copyable with trivial move/copy construction and
+/// trivial destruction. While the standard doesn't specify that you're allowed
+/// copy these types with memcpy, there is no way for the type to observe this.
+/// This catches the important case of std::pair<POD, POD>, which is not
+/// trivially assignable.
+///
+/// XXX: if build fails here fall back to C10_IS_TRIVIALLY_COPYABLE and make a
+/// note
+template <
+    typename T,
+    bool = (std::is_trivially_copy_constructible_v<T>)&&(
+        std::is_trivially_move_constructible_v<
+            T>)&&std::is_trivially_destructible_v<T>>
+class SmallVectorTemplateBase : public SmallVectorTemplateCommon<T> {
+  friend class SmallVectorTemplateCommon<T>;
+
+ protected:
+  static constexpr bool TakesParamByValue = false;
+  using ValueParamT = const T&;
+
+  SmallVectorTemplateBase(size_t Size) : SmallVectorTemplateCommon<T>(Size) {}
+
+  static void destroy_range(T* S, T* E) {
+    while (S != E) {
+      --E;
+      E->~T();
+    }
+  }
+
+  /// Move the range [I, E) into the uninitialized memory starting with "Dest",
+  /// constructing elements as needed.
+  template <typename It1, typename It2>
+  static void uninitialized_move(It1 I, It1 E, It2 Dest) {
+    std::uninitialized_copy(
+        std::make_move_iterator(I), std::make_move_iterator(E), Dest);
+  }
+
+  /// Copy the range [I, E) onto the uninitialized memory starting with "Dest",
+  /// constructing elements as needed.
+  template <typename It1, typename It2>
+  static void uninitialized_copy(It1 I, It1 E, It2 Dest) {
+    std::uninitialized_copy(I, E, Dest);
+  }
+
+  /// Grow the allocated memory (without initializing new elements), doubling
+  /// the size of the allocated memory. Guarantees space for at least one more
+  /// element, or MinSize more elements if specified.
+  void grow(size_t MinSize = 0);
+
+  /// Create a new allocation big enough for \p MinSize and pass back its size
+  /// in \p NewCapacity. This is the first section of \a grow().
+  T* mallocForGrow(size_t MinSize, size_t& NewCapacity) {
+    return static_cast<T*>(
+        SmallVectorBase<SmallVectorSizeType<T>>::mallocForGrow(
+            MinSize, sizeof(T), NewCapacity));
+  }
+
+  /// Move existing elements over to the new allocation \p NewElts, the middle
+  /// section of \a grow().
+  void moveElementsForGrow(T* NewElts);
+
+  /// Transfer ownership of the allocation, finishing up \a grow().
+  void takeAllocationForGrow(T* NewElts, size_t NewCapacity);
+
+  /// Reserve enough space to add one element, and return the updated element
+  /// pointer in case it was a reference to the storage.
+  const T* reserveForParamAndGetAddress(const T& Elt, size_t N = 1) {
+    return this->reserveForParamAndGetAddressImpl(this, Elt, N);
+  }
+
+  /// Reserve enough space to add one element, and return the updated element
+  /// pointer in case it was a reference to the storage.
+  T* reserveForParamAndGetAddress(T& Elt, size_t N = 1) {
+    return const_cast<T*>(this->reserveForParamAndGetAddressImpl(this, Elt, N));
+  }
+
+  static T&& forward_value_param(T&& V) {
+    return std::move(V);
+  }
+  static const T& forward_value_param(const T& V) {
+    return V;
+  }
+
+  void growAndAssign(size_t NumElts, const T& Elt) {
+    // Grow manually in case Elt is an internal reference.
+    size_t NewCapacity = 0;
+    T* NewElts = mallocForGrow(NumElts, NewCapacity);
+    std::uninitialized_fill_n(NewElts, NumElts, Elt);
+    this->destroy_range(this->begin(), this->end());
+    takeAllocationForGrow(NewElts, NewCapacity);
+    this->set_size(NumElts);
+  }
+
+  template <typename... ArgTypes>
+  T& growAndEmplaceBack(ArgTypes&&... Args) {
+    // Grow manually in case one of Args is an internal reference.
+    size_t NewCapacity = 0;
+    T* NewElts = mallocForGrow(0, NewCapacity);
+    ::new ((void*)(NewElts + this->size())) T(std::forward<ArgTypes>(Args)...);
+    moveElementsForGrow(NewElts);
+    takeAllocationForGrow(NewElts, NewCapacity);
+    this->set_size(this->size() + 1);
+    return this->back();
+  }
+
+ public:
+  void push_back(const T& Elt) {
+    const T* EltPtr = reserveForParamAndGetAddress(Elt);
+    ::new ((void*)this->end()) T(*EltPtr);
+    this->set_size(this->size() + 1);
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-rvalue-reference-param-not-moved)
+  void push_back(T&& Elt) {
+    T* EltPtr = reserveForParamAndGetAddress(Elt);
+    ::new ((void*)this->end()) T(::std::move(*EltPtr));
+    this->set_size(this->size() + 1);
+  }
+
+  void pop_back() {
+    this->set_size(this->size() - 1);
+    this->end()->~T();
+  }
+};
+
+// Define this out-of-line to dissuade the C++ compiler from inlining it.
+template <typename T, bool TriviallyCopyable>
+void SmallVectorTemplateBase<T, TriviallyCopyable>::grow(size_t MinSize) {
+  size_t NewCapacity = 0;
+  T* NewElts = mallocForGrow(MinSize, NewCapacity);
+  moveElementsForGrow(NewElts);
+  takeAllocationForGrow(NewElts, NewCapacity);
+}
+
+// Define this out-of-line to dissuade the C++ compiler from inlining it.
+template <typename T, bool TriviallyCopyable>
+void SmallVectorTemplateBase<T, TriviallyCopyable>::moveElementsForGrow(
+    T* NewElts) {
+  // Move the elements over.
+  this->uninitialized_move(this->begin(), this->end(), NewElts);
+
+  // Destroy the original elements.
+  destroy_range(this->begin(), this->end());
+}
+
+// Define this out-of-line to dissuade the C++ compiler from inlining it.
+template <typename T, bool TriviallyCopyable>
+void SmallVectorTemplateBase<T, TriviallyCopyable>::takeAllocationForGrow(
+    T* NewElts,
+    size_t NewCapacity) {
+  // If this wasn't grown from the inline copy, deallocate the old space.
+  if (!this->isSmall())
+    free(this->begin());
+
+  this->BeginX = NewElts;
+  this->Capacity = NewCapacity;
+}
+
+/// SmallVectorTemplateBase<TriviallyCopyable = true> - This is where we put
+/// method implementations that are designed to work with trivially copyable
+/// T's. This allows using memcpy in place of copy/move construction and
+/// skipping destruction.
+template <typename T>
+class SmallVectorTemplateBase<T, true> : public SmallVectorTemplateCommon<T> {
+  friend class SmallVectorTemplateCommon<T>;
+
+ protected:
+  /// True if it's cheap enough to take parameters by value. Doing so avoids
+  /// overhead related to mitigations for reference invalidation.
+  static constexpr bool TakesParamByValue = sizeof(T) <= 2 * sizeof(void*);
+
+  /// Either const T& or T, depending on whether it's cheap enough to take
+  /// parameters by value.
+  using ValueParamT = std::conditional_t<TakesParamByValue, T, const T&>;
+
+  SmallVectorTemplateBase(size_t Size) : SmallVectorTemplateCommon<T>(Size) {}
+
+  // No need to do a destroy loop for POD's.
+  static void destroy_range(T*, T*) {}
+
+  /// Move the range [I, E) onto the uninitialized memory
+  /// starting with "Dest", constructing elements into it as needed.
+  template <typename It1, typename It2>
+  static void uninitialized_move(It1 I, It1 E, It2 Dest) {
+    // Just do a copy.
+    uninitialized_copy(I, E, Dest);
+  }
+
+  /// Copy the range [I, E) onto the uninitialized memory
+  /// starting with "Dest", constructing elements into it as needed.
+  template <typename It1, typename It2>
+  static void uninitialized_copy(It1 I, It1 E, It2 Dest) {
+    // Arbitrary iterator types; just use the basic implementation.
+    std::uninitialized_copy(I, E, Dest);
+  }
+
+  /// Copy the range [I, E) onto the uninitialized memory
+  /// starting with "Dest", constructing elements into it as needed.
+  template <typename T1, typename T2>
+  static void uninitialized_copy(
+      T1* I,
+      T1* E,
+      T2* Dest,
+      std::enable_if_t<std::is_same_v<std::remove_const_t<T1>, T2>>* =
+          nullptr) {
+    // Use memcpy for PODs iterated by pointers (which includes SmallVector
+    // iterators): std::uninitialized_copy optimizes to memmove, but we can
+    // use memcpy here. Note that I and E are iterators and thus might be
+    // invalid for memcpy if they are equal.
+    if (I != E)
+      memcpy(reinterpret_cast<void*>(Dest), I, (E - I) * sizeof(T));
+  }
+
+  /// Double the size of the allocated memory, guaranteeing space for at
+  /// least one more element or MinSize if specified.
+  void grow(size_t MinSize = 0) {
+    this->grow_pod(MinSize, sizeof(T));
+  }
+
+  /// Reserve enough space to add one element, and return the updated element
+  /// pointer in case it was a reference to the storage.
+  const T* reserveForParamAndGetAddress(const T& Elt, size_t N = 1) {
+    return this->reserveForParamAndGetAddressImpl(this, Elt, N);
+  }
+
+  /// Reserve enough space to add one element, and return the updated element
+  /// pointer in case it was a reference to the storage.
+  T* reserveForParamAndGetAddress(T& Elt, size_t N = 1) {
+    return const_cast<T*>(this->reserveForParamAndGetAddressImpl(this, Elt, N));
+  }
+
+  /// Copy \p V or return a reference, depending on \a ValueParamT.
+  static ValueParamT forward_value_param(ValueParamT V) {
+    return V;
+  }
+
+  void growAndAssign(size_t NumElts, T Elt) {
+    // Elt has been copied in case it's an internal reference, side-stepping
+    // reference invalidation problems without losing the realloc optimization.
+    this->set_size(0);
+    this->grow(NumElts);
+    std::uninitialized_fill_n(this->begin(), NumElts, Elt);
+    this->set_size(NumElts);
+  }
+
+  template <typename... ArgTypes>
+  T& growAndEmplaceBack(ArgTypes&&... Args) {
+    // Use push_back with a copy in case Args has an internal reference,
+    // side-stepping reference invalidation problems without losing the realloc
+    // optimization.
+    push_back(T(std::forward<ArgTypes>(Args)...));
+    return this->back();
+  }
+
+ public:
+  void push_back(ValueParamT Elt) {
+    const T* EltPtr = reserveForParamAndGetAddress(Elt);
+    memcpy(reinterpret_cast<void*>(this->end()), EltPtr, sizeof(T));
+    this->set_size(this->size() + 1);
+  }
+
+  void pop_back() {
+    this->set_size(this->size() - 1);
+  }
+};
+
+/// This class consists of common code factored out of the SmallVector class to
+/// reduce code duplication based on the SmallVector 'N' template parameter.
+template <typename T>
+class SmallVectorImpl : public SmallVectorTemplateBase<T> {
+  using SuperClass = SmallVectorTemplateBase<T>;
+
+ public:
+  using iterator = typename SuperClass::iterator;
+  using const_iterator = typename SuperClass::const_iterator;
+  using reference = typename SuperClass::reference;
+  using size_type = typename SuperClass::size_type;
+
+ protected:
+  using SmallVectorTemplateBase<T>::TakesParamByValue;
+  using ValueParamT = typename SuperClass::ValueParamT;
+
+  // Default ctor - Initialize to empty.
+  explicit SmallVectorImpl(unsigned N) : SmallVectorTemplateBase<T>(N) {}
+
+ public:
+  SmallVectorImpl(const SmallVectorImpl&) = delete;
+
+  ~SmallVectorImpl() {
+    // Subclass has already destructed this vector's elements.
+    // If this wasn't grown from the inline copy, deallocate the old space.
+    if (!this->isSmall())
+      free(this->begin());
+  }
+
+  void clear() {
+    this->destroy_range(this->begin(), this->end());
+    this->Size = 0;
+  }
+
+ private:
+  template <bool ForOverwrite>
+  void resizeImpl(size_type N) {
+    if (N < this->size()) {
+      this->pop_back_n(this->size() - N);
+    } else if (N > this->size()) {
+      this->reserve(N);
+      for (auto I = this->end(), E = this->begin() + N; I != E; ++I)
+        if (ForOverwrite)
+          new (&*I) T;
+        else
+          new (&*I) T();
+      this->set_size(N);
+    }
+  }
+
+ public:
+  void resize(size_type N) {
+    resizeImpl<false>(N);
+  }
+
+  /// Like resize, but \ref T is POD, the new values won't be initialized.
+  void resize_for_overwrite(size_type N) {
+    resizeImpl<true>(N);
+  }
+
+  void resize(size_type N, ValueParamT NV) {
+    if (N == this->size())
+      return;
+
+    if (N < this->size()) {
+      this->pop_back_n(this->size() - N);
+      return;
+    }
+
+    // N > this->size(). Defer to append.
+    this->append(N - this->size(), NV);
+  }
+
+  void reserve(size_type N) {
+    if (this->capacity() < N)
+      this->grow(N);
+  }
+
+  void pop_back_n(size_type NumItems) {
+    assert(this->size() >= NumItems);
+    this->destroy_range(this->end() - NumItems, this->end());
+    this->set_size(this->size() - NumItems);
+  }
+
+  C10_NODISCARD T pop_back_val() {
+    T Result = ::std::move(this->back());
+    this->pop_back();
+    return Result;
+  }
+
+  void swap(SmallVectorImpl& RHS) noexcept;
+
+  /// Add the specified range to the end of the SmallVector.
+  template <
+      typename in_iter,
+      typename = std::enable_if_t<std::is_convertible_v<
+          typename std::iterator_traits<in_iter>::iterator_category,
+          std::input_iterator_tag>>>
+  void append(in_iter in_start, in_iter in_end) {
+    this->assertSafeToAddRange(in_start, in_end);
+    size_type NumInputs = std::distance(in_start, in_end);
+    this->reserve(this->size() + NumInputs);
+    this->uninitialized_copy(in_start, in_end, this->end());
+    this->set_size(this->size() + NumInputs);
+  }
+
+  /// Append \p NumInputs copies of \p Elt to the end.
+  void append(size_type NumInputs, ValueParamT Elt) {
+    const T* EltPtr = this->reserveForParamAndGetAddress(Elt, NumInputs);
+    std::uninitialized_fill_n(this->end(), NumInputs, *EltPtr);
+    this->set_size(this->size() + NumInputs);
+  }
+
+  void append(std::initializer_list<T> IL) {
+    append(IL.begin(), IL.end());
+  }
+
+  void append(const SmallVectorImpl& RHS) {
+    append(RHS.begin(), RHS.end());
+  }
+
+  void assign(size_type NumElts, ValueParamT Elt) {
+    // Note that Elt could be an internal reference.
+    if (NumElts > this->capacity()) {
+      this->growAndAssign(NumElts, Elt);
+      return;
+    }
+
+    // Assign over existing elements.
+    std::fill_n(this->begin(), std::min(NumElts, this->size()), Elt);
+    if (NumElts > this->size())
+      std::uninitialized_fill_n(this->end(), NumElts - this->size(), Elt);
+    else if (NumElts < this->size())
+      this->destroy_range(this->begin() + NumElts, this->end());
+    this->set_size(NumElts);
+  }
+
+  // FIXME: Consider assigning over existing elements, rather than clearing &
+  // re-initializing them - for all assign(...) variants.
+
+  template <
+      typename in_iter,
+      typename = std::enable_if_t<std::is_convertible_v<
+          typename std::iterator_traits<in_iter>::iterator_category,
+          std::input_iterator_tag>>>
+  void assign(in_iter in_start, in_iter in_end) {
+    this->assertSafeToReferenceAfterClear(in_start, in_end);
+    clear();
+    append(in_start, in_end);
+  }
+
+  void assign(std::initializer_list<T> IL) {
+    clear();
+    append(IL);
+  }
+
+  void assign(const SmallVectorImpl& RHS) {
+    assign(RHS.begin(), RHS.end());
+  }
+
+  iterator erase(iterator I) {
+    assert(
+        this->isReferenceToStorage(I) && "Iterator to erase is out of bounds.");
+
+    iterator N = I;
+    // Shift all elts down one.
+    std::move(I + 1, this->end(), I);
+    // Drop the last elt.
+    this->pop_back();
+    return (N);
+  }
+
+  iterator erase(iterator S, iterator E) {
+    assert(this->isRangeInStorage(S, E) && "Range to erase is out of bounds.");
+
+    iterator N = S;
+    // Shift all elts down.
+    iterator I = std::move(E, this->end(), S);
+    // Drop the last elts.
+    this->destroy_range(I, this->end());
+    this->set_size(I - this->begin());
+    return (N);
+  }
+
+ private:
+  template <class ArgType>
+  iterator insert_one_impl(iterator I, ArgType&& Elt) {
+    // Callers ensure that ArgType is derived from T.
+    static_assert(
+        std::is_same<std::remove_const_t<std::remove_reference_t<ArgType>>, T>::
+            value,
+        "ArgType must be derived from T!");
+
+    if (I == this->end()) { // Important special case for empty vector.
+      this->push_back(::std::forward<ArgType>(Elt));
+      return this->end() - 1;
+    }
+
+    assert(
+        this->isReferenceToStorage(I) &&
+        "Insertion iterator is out of bounds.");
+
+    // Grow if necessary.
+    size_t Index = I - this->begin();
+    std::remove_reference_t<ArgType>* EltPtr =
+        this->reserveForParamAndGetAddress(Elt);
+    I = this->begin() + Index;
+
+    ::new ((void*)this->end()) T(::std::move(this->back()));
+    // Push everything else over.
+    std::move_backward(I, this->end() - 1, this->end());
+    this->set_size(this->size() + 1);
+
+    // If we just moved the element we're inserting, be sure to update
+    // the reference (never happens if TakesParamByValue).
+    static_assert(
+        !TakesParamByValue || std::is_same<ArgType, T>::value,
+        "ArgType must be 'T' when taking by value!");
+    if (!TakesParamByValue && this->isReferenceToRange(EltPtr, I, this->end()))
+      ++EltPtr;
+
+    *I = ::std::forward<ArgType>(*EltPtr);
+    return I;
+  }
+
+ public:
+  iterator insert(iterator I, T&& Elt) {
+    return insert_one_impl(I, this->forward_value_param(std::move(Elt)));
+  }
+
+  iterator insert(iterator I, const T& Elt) {
+    return insert_one_impl(I, this->forward_value_param(Elt));
+  }
+
+  iterator insert(iterator I, size_type NumToInsert, ValueParamT Elt) {
+    // Convert iterator to elt# to avoid invalidating iterator when we reserve()
+    size_t InsertElt = I - this->begin();
+
+    if (I == this->end()) { // Important special case for empty vector.
+      append(NumToInsert, Elt);
+      return this->begin() + InsertElt;
+    }
+
+    assert(
+        this->isReferenceToStorage(I) &&
+        "Insertion iterator is out of bounds.");
+
+    // Ensure there is enough space, and get the (maybe updated) address of
+    // Elt.
+    const T* EltPtr = this->reserveForParamAndGetAddress(Elt, NumToInsert);
+
+    // Uninvalidate the iterator.
+    I = this->begin() + InsertElt;
+
+    // If there are more elements between the insertion point and the end of the
+    // range than there are being inserted, we can use a simple approach to
+    // insertion.  Since we already reserved space, we know that this won't
+    // reallocate the vector.
+    if (size_t(this->end() - I) >= NumToInsert) {
+      T* OldEnd = this->end();
+      append(
+          std::move_iterator<iterator>(this->end() - NumToInsert),
+          std::move_iterator<iterator>(this->end()));
+
+      // Copy the existing elements that get replaced.
+      std::move_backward(I, OldEnd - NumToInsert, OldEnd);
+
+      // If we just moved the element we're inserting, be sure to update
+      // the reference (never happens if TakesParamByValue).
+      if (!TakesParamByValue && I <= EltPtr && EltPtr < this->end())
+        EltPtr += NumToInsert;
+
+      std::fill_n(I, NumToInsert, *EltPtr);
+      return I;
+    }
+
+    // Otherwise, we're inserting more elements than exist already, and we're
+    // not inserting at the end.
+
+    // Move over the elements that we're about to overwrite.
+    T* OldEnd = this->end();
+    this->set_size(this->size() + NumToInsert);
+    size_t NumOverwritten = OldEnd - I;
+    this->uninitialized_move(I, OldEnd, this->end() - NumOverwritten);
+
+    // If we just moved the element we're inserting, be sure to update
+    // the reference (never happens if TakesParamByValue).
+    if (!TakesParamByValue && I <= EltPtr && EltPtr < this->end())
+      EltPtr += NumToInsert;
+
+    // Replace the overwritten part.
+    std::fill_n(I, NumOverwritten, *EltPtr);
+
+    // Insert the non-overwritten middle part.
+    std::uninitialized_fill_n(OldEnd, NumToInsert - NumOverwritten, *EltPtr);
+    return I;
+  }
+
+  template <
+      typename ItTy,
+      typename = std::enable_if_t<std::is_convertible_v<
+          typename std::iterator_traits<ItTy>::iterator_category,
+          std::input_iterator_tag>>>
+  iterator insert(iterator I, ItTy From, ItTy To) {
+    // Convert iterator to elt# to avoid invalidating iterator when we reserve()
+    size_t InsertElt = I - this->begin();
+
+    if (I == this->end()) { // Important special case for empty vector.
+      append(From, To);
+      return this->begin() + InsertElt;
+    }
+
+    assert(
+        this->isReferenceToStorage(I) &&
+        "Insertion iterator is out of bounds.");
+
+    // Check that the reserve that follows doesn't invalidate the iterators.
+    this->assertSafeToAddRange(From, To);
+
+    size_t NumToInsert = std::distance(From, To);
+
+    // Ensure there is enough space.
+    reserve(this->size() + NumToInsert);
+
+    // Uninvalidate the iterator.
+    I = this->begin() + InsertElt;
+
+    // If there are more elements between the insertion point and the end of the
+    // range than there are being inserted, we can use a simple approach to
+    // insertion.  Since we already reserved space, we know that this won't
+    // reallocate the vector.
+    if (size_t(this->end() - I) >= NumToInsert) {
+      T* OldEnd = this->end();
+      append(
+          std::move_iterator<iterator>(this->end() - NumToInsert),
+          std::move_iterator<iterator>(this->end()));
+
+      // Copy the existing elements that get replaced.
+      std::move_backward(I, OldEnd - NumToInsert, OldEnd);
+
+      std::copy(From, To, I);
+      return I;
+    }
+
+    // Otherwise, we're inserting more elements than exist already, and we're
+    // not inserting at the end.
+
+    // Move over the elements that we're about to overwrite.
+    T* OldEnd = this->end();
+    this->set_size(this->size() + NumToInsert);
+    size_t NumOverwritten = OldEnd - I;
+    this->uninitialized_move(I, OldEnd, this->end() - NumOverwritten);
+
+    // Replace the overwritten part.
+    for (T* J = I; NumOverwritten > 0; --NumOverwritten) {
+      *J = *From;
+      ++J;
+      ++From;
+    }
+
+    // Insert the non-overwritten middle part.
+    this->uninitialized_copy(From, To, OldEnd);
+    return I;
+  }
+
+  void insert(iterator I, std::initializer_list<T> IL) {
+    insert(I, IL.begin(), IL.end());
+  }
+
+  template <typename... ArgTypes>
+  reference emplace_back(ArgTypes&&... Args) {
+    if (C10_UNLIKELY(this->size() >= this->capacity()))
+      return this->growAndEmplaceBack(std::forward<ArgTypes>(Args)...);
+
+    ::new ((void*)this->end()) T(std::forward<ArgTypes>(Args)...);
+    this->set_size(this->size() + 1);
+    return this->back();
+  }
+
+  SmallVectorImpl& operator=(const SmallVectorImpl& RHS);
+
+  SmallVectorImpl& operator=(SmallVectorImpl&& RHS) noexcept(
+      std::is_nothrow_move_constructible_v<T> &&
+      std::is_nothrow_destructible_v<T>);
+
+  bool operator==(const SmallVectorImpl& RHS) const {
+    if (this->size() != RHS.size())
+      return false;
+    return std::equal(this->begin(), this->end(), RHS.begin());
+  }
+  bool operator!=(const SmallVectorImpl& RHS) const {
+    return !(*this == RHS);
+  }
+
+  bool operator<(const SmallVectorImpl& RHS) const {
+    return std::lexicographical_compare(
+        this->begin(), this->end(), RHS.begin(), RHS.end());
+  }
+};
+
+template <typename T>
+void SmallVectorImpl<T>::swap(SmallVectorImpl<T>& RHS) noexcept {
+  if (this == &RHS)
+    return;
+
+  // We can only avoid copying elements if neither vector is small.
+  if (!this->isSmall() && !RHS.isSmall()) {
+    std::swap(this->BeginX, RHS.BeginX);
+    std::swap(this->Size, RHS.Size);
+    std::swap(this->Capacity, RHS.Capacity);
+    return;
+  }
+  this->reserve(RHS.size());
+  RHS.reserve(this->size());
+
+  // Swap the shared elements.
+  size_t NumShared = this->size();
+  if (NumShared > RHS.size())
+    NumShared = RHS.size();
+  for (size_type i = 0; i != NumShared; ++i)
+    std::swap((*this)[i], RHS[i]);
+
+  // Copy over the extra elts.
+  if (this->size() > RHS.size()) {
+    size_t EltDiff = this->size() - RHS.size();
+    this->uninitialized_copy(this->begin() + NumShared, this->end(), RHS.end());
+    RHS.set_size(RHS.size() + EltDiff);
+    this->destroy_range(this->begin() + NumShared, this->end());
+    this->set_size(NumShared);
+  } else if (RHS.size() > this->size()) {
+    size_t EltDiff = RHS.size() - this->size();
+    this->uninitialized_copy(RHS.begin() + NumShared, RHS.end(), this->end());
+    this->set_size(this->size() + EltDiff);
+    this->destroy_range(RHS.begin() + NumShared, RHS.end());
+    RHS.set_size(NumShared);
+  }
+}
+
+template <typename T>
+SmallVectorImpl<T>& SmallVectorImpl<T>::operator=(
+    const SmallVectorImpl<T>& RHS) {
+  // Avoid self-assignment.
+  if (this == &RHS)
+    return *this;
+
+  // If we already have sufficient space, assign the common elements, then
+  // destroy any excess.
+  size_t RHSSize = RHS.size();
+  size_t CurSize = this->size();
+  if (CurSize >= RHSSize) {
+    // Assign common elements.
+    iterator NewEnd;
+    if (RHSSize)
+      NewEnd = std::copy(RHS.begin(), RHS.begin() + RHSSize, this->begin());
+    else
+      NewEnd = this->begin();
+
+    // Destroy excess elements.
+    this->destroy_range(NewEnd, this->end());
+
+    // Trim.
+    this->set_size(RHSSize);
+    return *this;
+  }
+
+  // If we have to grow to have enough elements, destroy the current elements.
+  // This allows us to avoid copying them during the grow.
+  // FIXME: don't do this if they're efficiently moveable.
+  if (this->capacity() < RHSSize) {
+    // Destroy current elements.
+    this->clear();
+    CurSize = 0;
+    this->grow(RHSSize);
+  } else if (CurSize) {
+    // Otherwise, use assignment for the already-constructed elements.
+    std::copy(RHS.begin(), RHS.begin() + CurSize, this->begin());
+  }
+
+  // Copy construct the new elements in place.
+  this->uninitialized_copy(
+      RHS.begin() + CurSize, RHS.end(), this->begin() + CurSize);
+
+  // Set end.
+  this->set_size(RHSSize);
+  return *this;
+}
+
+template <typename T>
+SmallVectorImpl<T>& SmallVectorImpl<T>::
+operator=(SmallVectorImpl<T>&& RHS) noexcept(
+    std::is_nothrow_move_constructible_v<T> &&
+    std::is_nothrow_destructible_v<T>) {
+  // Avoid self-assignment.
+  if (this == &RHS)
+    return *this;
+
+  // If the RHS isn't small, clear this vector and then steal its buffer.
+  if (!RHS.isSmall()) {
+    this->destroy_range(this->begin(), this->end());
+    if (!this->isSmall())
+      free(this->begin());
+    this->BeginX = RHS.BeginX;
+    this->Size = RHS.Size;
+    this->Capacity = RHS.Capacity;
+    RHS.resetToSmall();
+    return *this;
+  }
+
+  // If we already have sufficient space, assign the common elements, then
+  // destroy any excess.
+  size_t RHSSize = RHS.size();
+  size_t CurSize = this->size();
+  if (CurSize >= RHSSize) {
+    // Assign common elements.
+    iterator NewEnd = this->begin();
+    if (RHSSize)
+      NewEnd = std::move(RHS.begin(), RHS.end(), NewEnd);
+
+    // Destroy excess elements and trim the bounds.
+    this->destroy_range(NewEnd, this->end());
+    this->set_size(RHSSize);
+
+    // Clear the RHS.
+    RHS.clear();
+
+    return *this;
+  }
+
+  // If we have to grow to have enough elements, destroy the current elements.
+  // This allows us to avoid copying them during the grow.
+  // FIXME: this may not actually make any sense if we can efficiently move
+  // elements.
+  if (this->capacity() < RHSSize) {
+    // Destroy current elements.
+    this->clear();
+    CurSize = 0;
+    this->grow(RHSSize);
+  } else if (CurSize) {
+    // Otherwise, use assignment for the already-constructed elements.
+    std::move(RHS.begin(), RHS.begin() + CurSize, this->begin());
+  }
+
+  // Move-construct the new elements in place.
+  this->uninitialized_move(
+      RHS.begin() + CurSize, RHS.end(), this->begin() + CurSize);
+
+  // Set end.
+  this->set_size(RHSSize);
+
+  RHS.clear();
+  return *this;
+}
+
+/// Storage for the SmallVector elements.  This is specialized for the N=0 case
+/// to avoid allocating unnecessary storage.
+template <typename T, unsigned N>
+struct SmallVectorStorage {
+  alignas(T) char InlineElts[N * sizeof(T)];
+};
+
+/// We need the storage to be properly aligned even for small-size of 0 so that
+/// the pointer math in \a SmallVectorTemplateCommon::getFirstEl() is
+/// well-defined.
+template <typename T>
+struct alignas(T) SmallVectorStorage<T, 0> {};
+
+/// Forward declaration of SmallVector so that
+/// calculateSmallVectorDefaultInlinedElements can reference
+/// `sizeof(SmallVector<T, 0>)`.
+template <typename T, unsigned N>
+class /* LLVM_GSL_OWNER */ SmallVector;
+
+/// Helper class for calculating the default number of inline elements for
+/// `SmallVector<T>`.
+///
+/// This should be migrated to a constexpr function when our minimum
+/// compiler support is enough for multi-statement constexpr functions.
+template <typename T>
+struct CalculateSmallVectorDefaultInlinedElements {
+  // Parameter controlling the default number of inlined elements
+  // for `SmallVector<T>`.
+  //
+  // The default number of inlined elements ensures that
+  // 1. There is at least one inlined element.
+  // 2. `sizeof(SmallVector<T>) <= kPreferredSmallVectorSizeof` unless
+  // it contradicts 1.
+  static constexpr size_t kPreferredSmallVectorSizeof = 64;
+
+  // static_assert that sizeof(T) is not "too big".
+  //
+  // Because our policy guarantees at least one inlined element, it is possible
+  // for an arbitrarily large inlined element to allocate an arbitrarily large
+  // amount of inline storage. We generally consider it an antipattern for a
+  // SmallVector to allocate an excessive amount of inline storage, so we want
+  // to call attention to these cases and make sure that users are making an
+  // intentional decision if they request a lot of inline storage.
+  //
+  // We want this assertion to trigger in pathological cases, but otherwise
+  // not be too easy to hit. To accomplish that, the cutoff is actually somewhat
+  // larger than kPreferredSmallVectorSizeof (otherwise,
+  // `SmallVector<SmallVector<T>>` would be one easy way to trip it, and that
+  // pattern seems useful in practice).
+  //
+  // One wrinkle is that this assertion is in theory non-portable, since
+  // sizeof(T) is in general platform-dependent. However, we don't expect this
+  // to be much of an issue, because most LLVM development happens on 64-bit
+  // hosts, and therefore sizeof(T) is expected to *decrease* when compiled for
+  // 32-bit hosts, dodging the issue. The reverse situation, where development
+  // happens on a 32-bit host and then fails due to sizeof(T) *increasing* on a
+  // 64-bit host, is expected to be very rare.
+  static_assert(
+      sizeof(T) <= 256,
+      "You are trying to use a default number of inlined elements for "
+      "`SmallVector<T>` but `sizeof(T)` is really big! Please use an "
+      "explicit number of inlined elements with `SmallVector<T, N>` to make "
+      "sure you really want that much inline storage.");
+
+  // Discount the size of the header itself when calculating the maximum inline
+  // bytes.
+  static constexpr size_t PreferredInlineBytes =
+      kPreferredSmallVectorSizeof - sizeof(SmallVector<T, 0>);
+  static constexpr size_t NumElementsThatFit = PreferredInlineBytes / sizeof(T);
+  static constexpr size_t value =
+      NumElementsThatFit == 0 ? 1 : NumElementsThatFit;
+};
+
+/// This is a 'vector' (really, a variable-sized array), optimized
+/// for the case when the array is small.  It contains some number of elements
+/// in-place, which allows it to avoid heap allocation when the actual number of
+/// elements is below that threshold.  This allows normal "small" cases to be
+/// fast without losing generality for large inputs.
+///
+/// \note
+/// In the absence of a well-motivated choice for the number of inlined
+/// elements \p N, it is recommended to use \c SmallVector<T> (that is,
+/// omitting the \p N). This will choose a default number of inlined elements
+/// reasonable for allocation on the stack (for example, trying to keep \c
+/// sizeof(SmallVector<T>) around 64 bytes).
+///
+/// \warning This does not attempt to be exception safe.
+///
+/// \see https://llvm.org/docs/ProgrammersManual.html#llvm-adt-smallvector-h
+template <
+    typename T,
+    unsigned N = CalculateSmallVectorDefaultInlinedElements<T>::value>
+class /* LLVM_GSL_OWNER */ SmallVector : public SmallVectorImpl<T>,
+                                         SmallVectorStorage<T, N> {
+ public:
+  SmallVector() : SmallVectorImpl<T>(N) {}
+
+  ~SmallVector() {
+    // Destroy the constructed elements in the vector.
+    this->destroy_range(this->begin(), this->end());
+  }
+
+  explicit SmallVector(size_t Size, const T& Value = T())
+      : SmallVectorImpl<T>(N) {
+    this->assign(Size, Value);
+  }
+
+  template <
+      typename ItTy,
+      typename = std::enable_if_t<std::is_convertible_v<
+          typename std::iterator_traits<ItTy>::iterator_category,
+          std::input_iterator_tag>>>
+  SmallVector(ItTy S, ItTy E) : SmallVectorImpl<T>(N) {
+    this->append(S, E);
+  }
+
+  // note: The enable_if restricts Container to types that have a .begin() and
+  // .end() that return valid input iterators.
+  template <
+      typename Container,
+      std::enable_if_t<
+          std::is_convertible_v<
+              typename std::iterator_traits<
+                  decltype(std::declval<Container>()
+                               .begin())>::iterator_category,
+              std::input_iterator_tag> &&
+              std::is_convertible_v<
+                  typename std::iterator_traits<
+                      decltype(std::declval<Container>()
+                                   .end())>::iterator_category,
+                  std::input_iterator_tag>,
+          int> = 0>
+  explicit SmallVector(Container&& c) : SmallVectorImpl<T>(N) {
+    this->append(c.begin(), c.end());
+  }
+
+  SmallVector(std::initializer_list<T> IL) : SmallVectorImpl<T>(N) {
+    this->assign(IL);
+  }
+
+  SmallVector(const SmallVector& RHS) : SmallVectorImpl<T>(N) {
+    if (!RHS.empty())
+      SmallVectorImpl<T>::operator=(RHS);
+  }
+
+  SmallVector& operator=(const SmallVector& RHS) {
+    SmallVectorImpl<T>::operator=(RHS);
+    return *this;
+  }
+
+  SmallVector(SmallVector&& RHS) noexcept(
+      std::is_nothrow_move_assignable_v<SmallVectorImpl<T>>)
+      : SmallVectorImpl<T>(N) {
+    if (!RHS.empty())
+      SmallVectorImpl<T>::operator=(::std::move(RHS));
+  }
+
+  // note: The enable_if restricts Container to types that have a .begin() and
+  // .end() that return valid input iterators.
+  template <
+      typename Container,
+      std::enable_if_t<
+          std::is_convertible_v<
+              typename std::iterator_traits<
+                  decltype(std::declval<Container>()
+                               .begin())>::iterator_category,
+              std::input_iterator_tag> &&
+              std::is_convertible_v<
+                  typename std::iterator_traits<
+                      decltype(std::declval<Container>()
+                                   .end())>::iterator_category,
+                  std::input_iterator_tag>,
+          int> = 0>
+  SmallVector& operator=(const Container& RHS) {
+    this->assign(RHS.begin(), RHS.end());
+    return *this;
+  }
+
+  SmallVector(SmallVectorImpl<T>&& RHS) noexcept(
+      std::is_nothrow_move_assignable_v<SmallVectorImpl<T>>)
+      : SmallVectorImpl<T>(N) {
+    if (!RHS.empty())
+      SmallVectorImpl<T>::operator=(::std::move(RHS));
+  }
+
+  SmallVector& operator=(SmallVector&& RHS) noexcept(
+      std::is_nothrow_move_assignable_v<SmallVectorImpl<T>>) {
+    SmallVectorImpl<T>::operator=(::std::move(RHS));
+    return *this;
+  }
+
+  SmallVector& operator=(SmallVectorImpl<T>&& RHS) noexcept(
+      std::is_nothrow_move_constructible_v<SmallVectorImpl<T>>) {
+    SmallVectorImpl<T>::operator=(::std::move(RHS));
+    return *this;
+  }
+
+  // note: The enable_if restricts Container to types that have a .begin() and
+  // .end() that return valid input iterators.
+  template <
+      typename Container,
+      std::enable_if_t<
+          std::is_convertible_v<
+              typename std::iterator_traits<
+                  decltype(std::declval<Container>()
+                               .begin())>::iterator_category,
+              std::input_iterator_tag> &&
+              std::is_convertible_v<
+                  typename std::iterator_traits<
+                      decltype(std::declval<Container>()
+                                   .end())>::iterator_category,
+                  std::input_iterator_tag>,
+          int> = 0>
+  // NOLINTNEXTLINE(cppcoreguidelines-missing-std-forward)
+  SmallVector& operator=(Container&& C) {
+    this->assign(C.begin(), C.end());
+    return *this;
+  }
+
+  SmallVector& operator=(std::initializer_list<T> IL) {
+    this->assign(IL);
+    return *this;
+  }
+};
+
+template <typename T, unsigned N>
+inline size_t capacity_in_bytes(const SmallVector<T, N>& X) {
+  return X.capacity_in_bytes();
+}
+
+template <typename T, unsigned N>
+std::ostream& operator<<(std::ostream& out, const SmallVector<T, N>& list) {
+  int i = 0;
+  out << "[";
+  for (auto e : list) {
+    if (i++ > 0)
+      out << ", ";
+    out << e;
+  }
+  out << "]";
+  return out;
+}
+
+template <typename RangeType>
+using ValueTypeFromRangeType = std::remove_const_t<
+    std::remove_reference_t<decltype(*std::begin(std::declval<RangeType&>()))>>;
+
+/// Given a range of type R, iterate the entire range and return a
+/// SmallVector with elements of the vector.  This is useful, for example,
+/// when you want to iterate a range and then sort the results.
+template <unsigned Size, typename R>
+// NOLINTNEXTLINE(cppcoreguidelines-missing-std-forward)
+SmallVector<ValueTypeFromRangeType<R>, Size> to_vector(R&& Range) {
+  return {std::begin(Range), std::end(Range)};
+}
+template <typename R>
+SmallVector<
+    ValueTypeFromRangeType<R>,
+    CalculateSmallVectorDefaultInlinedElements<
+        ValueTypeFromRangeType<R>>::value>
+// NOLINTNEXTLINE(cppcoreguidelines-missing-std-forward)
+to_vector(R&& Range) {
+  return {std::begin(Range), std::end(Range)};
+}
+
+} // end namespace c10
+
+namespace std {
+
+/// Implement std::swap in terms of SmallVector swap.
+template <typename T>
+inline void swap(
+    c10::SmallVectorImpl<T>& LHS,
+    c10::SmallVectorImpl<T>& RHS) noexcept {
+  LHS.swap(RHS);
+}
+
+/// Implement std::swap in terms of SmallVector swap.
+template <typename T, unsigned N>
+inline void swap(
+    c10::SmallVector<T, N>& LHS,
+    c10::SmallVector<T, N>& RHS) noexcept {
+  LHS.swap(RHS);
+}
+
+} // end namespace std

videochat2/lib/python3.10/site-packages/torch/include/c10/util/ThreadLocal.h

@@ -0,0 +1,153 @@
+#pragma once
+
+#include <c10/macros/Macros.h>
+
+/**
+ * Android versions with libgnustl incorrectly handle thread_local C++
+ * qualifier with composite types. NDK up to r17 version is affected.
+ *
+ * (A fix landed on Jun 4 2018:
+ * https://android-review.googlesource.com/c/toolchain/gcc/+/683601)
+ *
+ * In such cases, use c10::ThreadLocal<T> wrapper
+ * which is `pthread_*` based with smart pointer semantics.
+ *
+ * In addition, convenient macro C10_DEFINE_TLS_static is available.
+ * To define static TLS variable of type std::string, do the following
+ * ```
+ *  C10_DEFINE_TLS_static(std::string, str_tls_);
+ *  ///////
+ *  {
+ *    *str_tls_ = "abc";
+ *    assert(str_tls_->length(), 3);
+ *  }
+ * ```
+ *
+ * (see c10/test/util/ThreadLocal_test.cpp for more examples)
+ */
+#if !defined(C10_PREFER_CUSTOM_THREAD_LOCAL_STORAGE)
+
+#if defined(C10_ANDROID) && defined(__GLIBCXX__) && __GLIBCXX__ < 20180604
+#define C10_PREFER_CUSTOM_THREAD_LOCAL_STORAGE
+#endif // defined(C10_ANDROID) && defined(__GLIBCXX__) && __GLIBCXX__ < 20180604
+
+#endif // !defined(C10_PREFER_CUSTOM_THREAD_LOCAL_STORAGE)
+
+#if defined(C10_PREFER_CUSTOM_THREAD_LOCAL_STORAGE)
+#include <c10/util/Exception.h>
+#include <errno.h>
+#include <pthread.h>
+#include <memory>
+namespace c10 {
+
+/**
+ * @brief Temporary thread_local C++ qualifier replacement for Android
+ * based on `pthread_*`.
+ * To be used with composite types that provide default ctor.
+ */
+template <typename Type>
+class ThreadLocal {
+ public:
+  ThreadLocal() {
+    pthread_key_create(
+        &key_, [](void* buf) { delete static_cast<Type*>(buf); });
+  }
+
+  ~ThreadLocal() {
+    if (void* current = pthread_getspecific(key_)) {
+      delete static_cast<Type*>(current);
+    }
+
+    pthread_key_delete(key_);
+  }
+
+  ThreadLocal(const ThreadLocal&) = delete;
+  ThreadLocal& operator=(const ThreadLocal&) = delete;
+
+  Type& get() {
+    if (void* current = pthread_getspecific(key_)) {
+      return *static_cast<Type*>(current);
+    }
+
+    std::unique_ptr<Type> ptr = std::make_unique<Type>();
+    if (0 == pthread_setspecific(key_, ptr.get())) {
+      return *ptr.release();
+    }
+
+    int err = errno;
+    TORCH_INTERNAL_ASSERT(false, "pthread_setspecific() failed, errno = ", err);
+  }
+
+  Type& operator*() {
+    return get();
+  }
+
+  Type* operator->() {
+    return &get();
+  }
+
+ private:
+  pthread_key_t key_;
+};
+
+} // namespace c10
+
+#define C10_DEFINE_TLS_static(Type, Name) static ::c10::ThreadLocal<Type> Name
+
+#define C10_DECLARE_TLS_class_static(Class, Type, Name) \
+  static ::c10::ThreadLocal<Type> Name
+
+#define C10_DEFINE_TLS_class_static(Class, Type, Name) \
+  ::c10::ThreadLocal<Type> Class::Name
+
+#else // defined(C10_PREFER_CUSTOM_THREAD_LOCAL_STORAGE)
+
+namespace c10 {
+
+/**
+ * @brief Default thread_local implementation for non-Android cases.
+ * To be used with composite types that provide default ctor.
+ */
+template <typename Type>
+class ThreadLocal {
+ public:
+  using Accessor = Type* (*)();
+  explicit ThreadLocal(Accessor accessor) : accessor_(accessor) {}
+
+  ThreadLocal(const ThreadLocal&) = delete;
+  ThreadLocal& operator=(const ThreadLocal&) = delete;
+
+  Type& get() {
+    return *accessor_();
+  }
+
+  Type& operator*() {
+    return get();
+  }
+
+  Type* operator->() {
+    return &get();
+  }
+
+ private:
+  Accessor accessor_;
+};
+
+} // namespace c10
+
+#define C10_DEFINE_TLS_static(Type, Name)     \
+  static ::c10::ThreadLocal<Type> Name([]() { \
+    static thread_local Type var;             \
+    return &var;                              \
+  })
+
+#define C10_DECLARE_TLS_class_static(Class, Type, Name) \
+  static ::c10::ThreadLocal<Type> Name
+
+#define C10_DEFINE_TLS_class_static(Class, Type, Name) \
+  ::c10::ThreadLocal<Type> Class::Name([]() {          \
+    static thread_local Type var;                      \
+    return &var;                                       \
+  })
+
+#endif // defined(C10_PREFER_CUSTOM_THREAD_LOCAL_STORAGE)

videochat2/lib/python3.10/site-packages/torch/include/c10/util/TypeCast.h

@@ -0,0 +1,195 @@
+#pragma once
+#include <c10/macros/Macros.h>
+#include <c10/util/BFloat16.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/complex.h>
+
+#include <type_traits>
+
+C10_CLANG_DIAGNOSTIC_PUSH()
+#if C10_CLANG_HAS_WARNING("-Wimplicit-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-float-conversion")
+#endif
+#if C10_CLANG_HAS_WARNING("-Wimplicit-int-float-conversion")
+C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-int-float-conversion")
+#endif
+
+namespace c10 {
+
+template <typename dest_t, typename src_t>
+struct needs_real {
+  constexpr static bool value =
+      (is_complex<src_t>::value && !is_complex<dest_t>::value);
+};
+
+template <bool, typename src_t>
+struct maybe_real {
+  C10_HOST_DEVICE static inline src_t apply(src_t src) {
+    return src;
+  }
+};
+
+template <typename src_t>
+struct maybe_real<true, src_t> {
+  C10_HOST_DEVICE static inline decltype(auto) apply(src_t src) {
+    return src.real();
+  }
+};
+
+template <bool, typename src_t>
+struct maybe_bool {
+  C10_HOST_DEVICE static inline src_t apply(src_t src) {
+    return src;
+  }
+};
+
+template <typename src_t>
+struct maybe_bool<true, src_t> {
+  C10_HOST_DEVICE static inline decltype(auto) apply(src_t src) {
+    // Don't use bool operator so as to to also compile for ComplexHalf.
+    return src.real() || src.imag();
+  }
+};
+
+// Note: deliberately ignores undefined behavior, consistent with NumPy.
+// PyTorch's type conversions can cause a variety of undefined behavior,
+// including float to integral overflow and signed to unsigned integer overflow.
+// Some of this undefined behavior is addressed below.
+template <typename dest_t, typename src_t>
+struct static_cast_with_inter_type {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline dest_t apply(
+      src_t src) {
+    constexpr bool real = needs_real<dest_t, src_t>::value;
+    auto r = maybe_real<real, src_t>::apply(src);
+    return static_cast<dest_t>(r);
+  }
+};
+
+// Partial template specialization for casting to bool.
+// Need to handle complex types separately, as we don't
+// simply want to cast the real part to bool.
+template <typename src_t>
+struct static_cast_with_inter_type<bool, src_t> {
+  C10_HOST_DEVICE static inline bool apply(src_t src) {
+    constexpr bool complex = needs_real<bool, src_t>::value;
+    return static_cast<bool>(maybe_bool<complex, src_t>::apply(src));
+  }
+};
+
+// Partial template instantiation for casting to uint8.
+// Note: Converting from negative float values to unsigned integer types is
+// undefined behavior in C++, and current CPU and GPU compilers exhibit
+// divergent behavior. Casting from negative float values to signed
+// integer types and then to unsigned integer types is not undefined,
+// however, so this cast improves the consistency of type conversions
+// to uint8 across compilers.
+// Further note: Type conversions across compilers still have other undefined
+// and divergent behavior.
+template <typename src_t>
+struct static_cast_with_inter_type<uint8_t, src_t> {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline uint8_t apply(
+      src_t src) {
+    constexpr bool real = needs_real<uint8_t, src_t>::value;
+    return static_cast<uint8_t>(
+        static_cast<int64_t>(maybe_real<real, src_t>::apply(src)));
+  }
+};
+
+template <>
+struct static_cast_with_inter_type<c10::complex<c10::Half>, c10::BFloat16> {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline c10::complex<
+      c10::Half>
+  apply(c10::BFloat16 src) {
+    return static_cast<c10::complex<c10::Half>>(c10::complex<float>{src});
+  }
+};
+
+template <>
+struct static_cast_with_inter_type<c10::complex<c10::Half>, c10::Float8_e5m2> {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline c10::complex<
+      c10::Half>
+  apply(c10::Float8_e5m2 src) {
+    return static_cast<c10::complex<c10::Half>>(c10::complex<float>{src});
+  }
+};
+
+template <>
+struct static_cast_with_inter_type<
+    c10::complex<c10::Half>,
+    c10::Float8_e5m2fnuz> {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline c10::complex<
+      c10::Half>
+  apply(c10::Float8_e5m2fnuz src) {
+    return static_cast<c10::complex<c10::Half>>(c10::complex<float>{src});
+  }
+};
+
+template <>
+struct static_cast_with_inter_type<
+    c10::complex<c10::Half>,
+    c10::Float8_e4m3fn> {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline c10::complex<
+      c10::Half>
+  apply(c10::Float8_e4m3fn src) {
+    return static_cast<c10::complex<c10::Half>>(c10::complex<float>{src});
+  }
+};
+
+template <>
+struct static_cast_with_inter_type<
+    c10::complex<c10::Half>,
+    c10::Float8_e4m3fnuz> {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline c10::complex<
+      c10::Half>
+  apply(c10::Float8_e4m3fnuz src) {
+    return static_cast<c10::complex<c10::Half>>(c10::complex<float>{src});
+  }
+};
+
+template <>
+struct static_cast_with_inter_type<c10::complex<c10::Half>, c10::Half> {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline c10::complex<
+      c10::Half>
+  apply(c10::Half src) {
+    return static_cast<c10::complex<c10::Half>>(c10::complex<float>{src});
+  }
+};
+
+template <>
+struct static_cast_with_inter_type<
+    c10::complex<c10::Half>,
+    c10::complex<double>> {
+  C10_HOST_DEVICE __ubsan_ignore_undefined__ static inline c10::complex<
+      c10::Half>
+  apply(c10::complex<double> src) {
+    return static_cast<c10::complex<c10::Half>>(
+        static_cast<c10::complex<float>>(src));
+  }
+};
+
+template <typename To, typename From>
+C10_HOST_DEVICE To convert(From f) {
+  return static_cast_with_inter_type<To, From>::apply(f);
+}
+
+// Define separately to avoid being inlined and prevent code-size bloat
+[[noreturn]] C10_API void report_overflow(const char* name);
+
+template <typename To, typename From>
+To checked_convert(From f, const char* name) {
+  // Converting to bool can't overflow so we exclude this case from checking.
+  if (!std::is_same_v<To, bool> && overflows<To, From>(f)) {
+    report_overflow(name);
+  }
+  return convert<To, From>(f);
+}
+
+} // namespace c10
+
+C10_CLANG_DIAGNOSTIC_POP()
+
+// Trigger tests for D25440771. TODO: Remove this line any time you want.

videochat2/lib/python3.10/site-packages/torch/include/c10/util/TypeIndex.h

@@ -0,0 +1,196 @@
+#pragma once
+
+#include <c10/util/ConstexprCrc.h>
+#include <c10/util/IdWrapper.h>
+#include <c10/util/string_view.h>
+#include <cstdint>
+#include <ostream>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+
+namespace c10::util {
+
+// TODO Make it work for more compilers
+
+// Intel compiler works
+#if defined(__INTEL_COMPILER)
+#define C10_TYPENAME_SUPPORTS_CONSTEXPR 0
+#define C10_TYPENAME_CONSTEXPR
+
+// Clang works
+#elif defined(__clang__)
+
+// except for NVCC
+#if defined(__CUDACC__)
+#define C10_TYPENAME_SUPPORTS_CONSTEXPR 0
+#define C10_TYPENAME_CONSTEXPR
+#else
+#define C10_TYPENAME_SUPPORTS_CONSTEXPR 1
+#define C10_TYPENAME_CONSTEXPR constexpr
+#endif
+
+// Windows works
+#elif defined(_MSC_VER)
+
+// except for NVCC
+#if defined(__CUDACC__)
+#define C10_TYPENAME_SUPPORTS_CONSTEXPR 0
+#define C10_TYPENAME_CONSTEXPR
+#else
+#define C10_TYPENAME_SUPPORTS_CONSTEXPR 1
+#define C10_TYPENAME_CONSTEXPR constexpr
+#endif
+
+// GCC works
+#elif defined(__GNUC__)
+
+// except when gcc < 9
+#if (__GNUC__ < 9) || defined(__CUDACC__)
+#define C10_TYPENAME_SUPPORTS_CONSTEXPR 0
+#define C10_TYPENAME_CONSTEXPR
+#else
+#define C10_TYPENAME_SUPPORTS_CONSTEXPR 1
+#define C10_TYPENAME_CONSTEXPR constexpr
+#endif
+
+// some other compiler we don't know about
+#else
+#define C10_TYPENAME_SUPPORTS_CONSTEXPR 1
+#define C10_TYPENAME_CONSTEXPR constexpr
+#endif
+
+struct type_index final : IdWrapper<type_index, uint64_t> {
+  constexpr explicit type_index(uint64_t checksum) : IdWrapper(checksum) {}
+
+  // Allow usage in std::map / std::set
+  // TODO Disallow this and rather use std::unordered_map/set everywhere
+  friend constexpr bool operator<(type_index lhs, type_index rhs) noexcept {
+    return lhs.underlyingId() < rhs.underlyingId();
+  }
+
+  friend std::ostream& operator<<(std::ostream& stream, type_index typeId) {
+    return stream << typeId.underlyingId();
+  }
+};
+
+namespace detail {
+
+#if !defined(__clang__) && !defined(_MSC_VER) && defined(__GNUC__) && \
+    __GNUC__ < 5
+// Getting __PRETTY_FUNCTION__ at compile time only works with GCC >= 5
+#error "You're running a too old version of GCC. We need GCC 5 or later."
+#endif
+
+#if defined(__clang__) && __clang_major__ < 4
+// Getting __PRETTY_FUNCTION__ at compile time only works with Clang >= 4
+#error "You're running a too old version of Clang. We need Clang 4 or later."
+#endif
+
+inline constexpr string_view extract(
+    string_view prefix,
+    string_view suffix,
+    string_view str) {
+#if !defined(__CUDA_ARCH__) // CUDA doesn't like std::logic_error in device code
+  return (!str.starts_with(prefix) || !str.ends_with(suffix))
+      ? (throw std::logic_error("Invalid pattern"), string_view())
+      : str.substr(prefix.size(), str.size() - prefix.size() - suffix.size());
+#else
+  return str.substr(prefix.size(), str.size() - prefix.size() - suffix.size());
+#endif
+}
+
+template <typename T>
+inline C10_TYPENAME_CONSTEXPR c10::string_view fully_qualified_type_name_impl() {
+#if defined(_MSC_VER) && !defined(__clang__)
+#if defined(__NVCC__)
+  return extract(
+      "c10::basic_string_view<char> c10::util::detail::fully_qualified_type_name_impl<",
+      ">()",
+      __FUNCSIG__);
+#else
+  return extract(
+      "class c10::basic_string_view<char> __cdecl c10::util::detail::fully_qualified_type_name_impl<",
+      ">(void)",
+      __FUNCSIG__);
+#endif
+#elif defined(__clang__)
+  return extract(
+      "c10::string_view c10::util::detail::fully_qualified_type_name_impl() [T = ",
+      "]",
+      __PRETTY_FUNCTION__);
+#elif defined(__GNUC__)
+  return extract(
+#if C10_TYPENAME_SUPPORTS_CONSTEXPR
+      "constexpr c10::string_view c10::util::detail::fully_qualified_type_name_impl() [with T = ",
+#else
+      "c10::string_view c10::util::detail::fully_qualified_type_name_impl() [with T = ",
+#endif
+      "; c10::string_view = c10::basic_string_view<char>]",
+      __PRETTY_FUNCTION__);
+#endif
+}
+
+#if !defined(__CUDA_ARCH__)
+template <typename T>
+inline constexpr uint64_t type_index_impl() {
+// Idea: __PRETTY_FUNCTION__ (or __FUNCSIG__ on msvc) contains a qualified name
+// of this function, including its template parameter, i.e. including the
+// type we want an id for. We use this name and run crc64 on it to get a type
+// id.
+#if defined(_MSC_VER) && !defined(__clang__)
+  return crc64(__FUNCSIG__, sizeof(__FUNCSIG__)).checksum();
+#elif defined(__clang__)
+  return crc64(__PRETTY_FUNCTION__, sizeof(__PRETTY_FUNCTION__)).checksum();
+#elif defined(__GNUC__)
+  return crc64(__PRETTY_FUNCTION__, sizeof(__PRETTY_FUNCTION__)).checksum();
+#endif
+}
+#endif
+
+} // namespace detail
+
+template <typename T>
+inline constexpr type_index get_type_index() {
+#if !defined(__CUDA_ARCH__)
+  // To enforce that this is really computed at compile time, we pass the
+  // type index through std::integral_constant.
+  return type_index{std::integral_constant<
+      uint64_t,
+      detail::type_index_impl<std::decay_t<T>>()>::value};
+#else
+  // There's nothing in theory preventing us from running this on device code
+  // except for nvcc throwing a compiler error if we enable it.
+  return (abort(), type_index(0));
+#endif
+}
+
+#if !defined(TORCH_PEDANTIC)
+// Use precomputed hashsum for std::string
+// Needed to workaround ambiguity in class name resolution
+// into __PRETTY_FUNCTION__ when abovementioned class is defined in inlined
+// namespace. In multi-ABI C++ library, `std::string` is an alias to
+// `std::__cxx11::basic_string<char>` which depending on compiler flags can be
+// resolved to `basic_string<char>` either in `std` namespace or in
+// `std::__cxx11` one (`__cxx11` is an inline namespace)
+template <>
+inline constexpr type_index get_type_index<std::string>() {
+  // hashsum for std::basic_string<char>
+  return type_index{4193213214807308375ULL};
+}
+#endif
+
+template <typename T>
+inline C10_TYPENAME_CONSTEXPR string_view
+get_fully_qualified_type_name() noexcept {
+#if C10_TYPENAME_SUPPORTS_CONSTEXPR
+  constexpr
+#else
+  static
+#endif
+      string_view name = detail::fully_qualified_type_name_impl<T>();
+  return name;
+}
+} // namespace c10::util
+
+C10_DEFINE_HASH_FOR_IDWRAPPER(c10::util::type_index);