134 lines
6.5 KiB
C++
134 lines
6.5 KiB
C++
#pragma once
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#include <cstdint>
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namespace caffe2 {
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namespace serialize {
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constexpr uint64_t kMinSupportedFileFormatVersion = 0x1L;
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constexpr uint64_t kMaxSupportedFileFormatVersion = 0xAL;
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// Versions (i.e. why was the version number bumped?)
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// Note [Dynamic Versions and torch.jit.save vs. torch.save]
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//
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// Our versioning scheme has a "produced file format version" which
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// describes how an archive is to be read. The version written in an archive
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// is at least this current produced file format version, but may be greater
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// if it includes certain symbols. We refer to these conditional versions
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// as "dynamic," since they are identified at runtime.
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//
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// Dynamic versioning is useful when an operator's semantics are updated.
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// When using torch.jit.save we want those semantics to be preserved. If
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// we bumped the produced file format version on every change, however,
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// then older versions of PyTorch couldn't read even simple archives, like
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// a single tensor, from newer versions of PyTorch. Instead, we
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// assign dynamic versions to these changes that override the
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// produced file format version as needed. That is, when the semantics
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// of torch.div changed it was assigned dynamic version 4, and when
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// torch.jit.saving modules that use torch.div those archives also have
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// (at least) version 4. This prevents earlier versions of PyTorch
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// from accidentally performing the wrong kind of division. Modules
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// that don't use torch.div or other operators with dynamic versions
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// can write the produced file format version, and these programs will
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// run as expected on earlier versions of PyTorch.
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//
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// While torch.jit.save attempts to preserve operator semantics,
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// torch.save does not. torch.save is analogous to pickling Python, so
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// a function that uses torch.div will have different behavior if torch.saved
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// and torch.loaded across PyTorch versions. From a technical perspective,
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// torch.save ignores dynamic versioning.
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// 1. Initial version
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// 2. Removed op_version_set version numbers
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// 3. Added type tags to pickle serialization of container types
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// 4. (Dynamic) Stopped integer division using torch.div
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// (a versioned symbol preserves the historic behavior of versions 1--3)
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// 5. (Dynamic) Stops torch.full inferring a floating point dtype
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// when given bool or integer fill values.
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// 6. Write version string to `./data/version` instead of `version`.
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// [12/15/2021]
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// kProducedFileFormatVersion is set to 7 from 3 due to a different
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// interpretation of what file format version is.
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// Whenever there is new upgrader introduced,
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// this number should be bumped.
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// The reasons that version is bumped in the past:
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// 1. aten::div is changed at version 4
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// 2. aten::full is changed at version 5
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// 3. torch.package uses version 6
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// 4. Introduce new upgrader design and set the version number to 7
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// mark this change
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// --------------------------------------------------
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// We describe new operator version bump reasons here:
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// 1) [01/24/2022]
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// We bump the version number to 8 to update aten::linspace
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// and aten::linspace.out to error out when steps is not
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// provided. (see: https://github.com/pytorch/pytorch/issues/55951)
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// 2) [01/30/2022]
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// Bump the version number to 9 to update aten::logspace and
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// and aten::logspace.out to error out when steps is not
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// provided. (see: https://github.com/pytorch/pytorch/issues/55951)
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// 3) [02/11/2022]
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// Bump the version number to 10 to update aten::gelu and
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// and aten::gelu.out to support the new approximate kwarg.
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// (see: https://github.com/pytorch/pytorch/pull/61439)
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constexpr uint64_t kProducedFileFormatVersion = 0xAL;
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// Absolute minimum version we will write packages. This
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// means that every package from now on will always be
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// greater than this number.
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constexpr uint64_t kMinProducedFileFormatVersion = 0x3L;
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// The version we write when the archive contains bytecode.
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// It must be higher or eq to kProducedFileFormatVersion.
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// Because torchscript changes is likely introduce bytecode change.
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// If kProducedFileFormatVersion is increased, kProducedBytecodeVersion
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// should be increased too. The relationship is:
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// kMaxSupportedFileFormatVersion >= (most likely ==) kProducedBytecodeVersion
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// >= kProducedFileFormatVersion
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// If a format change is forward compatible (still readable by older
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// executables), we will not increment the version number, to minimize the
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// risk of breaking existing clients. TODO: A better way would be to allow
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// the caller that creates a model to specify a maximum version that its
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// clients can accept.
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// Versions:
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// 0x1L: Initial version
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// 0x2L: (Comment missing)
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// 0x3L: (Comment missing)
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// 0x4L: (update) Added schema to function tuple. Forward-compatible change.
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// 0x5L: (update) Update bytecode is sharing constant tensor files from
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// torchscript, and only serialize extra tensors that are not in the
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// torchscript constant table. Also update tensor storage schema adapting to
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// the unify format, the root key of tensor storage is updated from {index} to
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// {the_pointer_value_the_tensor.storage}, for example:
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// `140245072983168.storage` Forward-compatibility change.
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// 0x6L: Implicit opereator versioning using number of specified argument.
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// Refer to the summary of https://github.com/pytorch/pytorch/pull/56845 for
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// details.
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// 0x7L: Enable support for operators with default arguments plus out
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// arguments. Refer. See https://github.com/pytorch/pytorch/pull/63651 for
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// details.
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// 0x8L: Emit promoted operators as instructions. See
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// https://github.com/pytorch/pytorch/pull/71662 for details.
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// 0x9L: Change serialization format from pickle to format This version is to
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// serve migration. v8 pickle and v9 flatbuffer are the same. Refer to the
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// summary of https://github.com/pytorch/pytorch/pull/75201 for more details.
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constexpr uint64_t kProducedBytecodeVersion = 0x8L;
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// static_assert(
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// kProducedBytecodeVersion >= kProducedFileFormatVersion,
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// "kProducedBytecodeVersion must be higher or equal to
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// kProducedFileFormatVersion.");
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// Introduce kMinSupportedBytecodeVersion and kMaxSupportedBytecodeVersion
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// for limited backward/forward compatibility support of bytecode. If
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// kMinSupportedBytecodeVersion <= model_version <= kMaxSupportedBytecodeVersion
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// (in loader), we should support this model_version. For example, we provide a
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// wrapper to handle an updated operator.
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constexpr uint64_t kMinSupportedBytecodeVersion = 0x4L;
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constexpr uint64_t kMaxSupportedBytecodeVersion = 0x9L;
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} // namespace serialize
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} // namespace caffe2
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