Persistent Cache{#dev_guide_persistent_cache}
===========================================================

Creating oneDNN abstractions can be costly for various reasons.
Usually, oneDNN mitigates that overhead by caching such objects but
cache has no effect when the objects are created for the first time.
For some applications, reducing that overhead is critical.

oneDNN provides an API that can be used to create a persistent cache for
oneDNN abstractions. Through the API, users can obtain a cache blob ID
and a cache blob to use as a key and value respectively.

@note
Content and size of the cache blob ID and cache blob objects are not specified.

@note
The oneDNN version and git commit hash (@ref dnnl_version_t::hash) affect the equality
of the cache blob IDs. That is, the queried cache blob ID will differ
for different oneDNN versions and git commit hashes.

@warning
The git commit hash may not be available if the git package is not found during
a CMake call. In this case, the cache blob ID will be the same for different
hashes. This may result in fetching a wrong cache blob from persistent cache.


## Primitive

* The cache blob ID can be obtained via @ref dnnl::engine dnnl::primitive_desc_base::get_cache_blob_id
* The cache blob can be obtained via @ref dnnl::primitive::get_cache_blob
* Each primitive class provides a constructor that takes the cache blob along
with the primitive descriptor.


### Relation to Primitive Cache
When a primitive is created from a cache blob and the identical
primitive is present in the primitive cache, the one from primitive cache will
be returned to the user, and the given cache blob will not be used. Otherwise,
the cache blob will be used to speed up the primitive creation. The information
about how the primitive was created (`cache_miss`, `cache_hit` or
`persistent_cache_hit`) is part of the verbose output for verbose level 2
(@ref dev_guide_verbose).

### API Usage Example

The following pseudo-code demonstrates a simple example of persistent cache
implementation for primitives using the oneDNN API:

~~~cpp
using namespace dnnl;

{
    convolution_forward::primitive_desc conv_pd(desc, attr, engine);
    convolution_forward conv(conv_pd);

    std::vector<uint8_t> key = conv_pd.get_cache_blob_id();
    std::vector<uint8_t> value = conv.get_cache_blob();
    store_cache_blob_on_disk(key, value);
}

{
    convolution_forward::primitive_desc conv_pd(desc, attr, engine);
    std::vector<uint8_t> key = conv_pd.get_cache_blob_id();
    std::vector<uint8_t> value = load_cache_blob_from_disk(key);
    convolution_forward conv_from_cache_blob(conv_pd, value);
}
~~~

## Engine

* The cache blob ID can be obtained via @ref dnnl::ocl_interop::get_engine_cache_blob_id
* The cache blob can obtained via @ref dnnl::ocl_interop::get_engine_cache_blob
* The engine can be created with the cache blob via @ref dnnl::ocl_interop::make_engine(cl_device_id, cl_context, const std::vector<uint8_t> &)

### API Usage Example

The following pseudo-code demonstrates a simple example of persistent cache
implementation for OpenCL engines using the oneDNN API:

~~~cpp
using namespace dnnl;

{
    cl_device_id device = ...;
    cl_context context = ...;

    engine ocl_engine = ocl_interop::make_engine(device, context);
    std::vector<uint8_t> key = get_engine_cache_blob_id(ocl_interop::get_device(ocl_engine));
    std::vector<uint8_t> value = get_engine_cache_blob(ocl_engine);

    store_cache_blob_on_disk(key, value);
}

{
    cl_device_id device = ...;
    cl_context context = ...;

    std::vector<uint8_t> key = get_engine_cache_blob_id(device);
    std::vector<uint8_t> value = load_cache_blob_from_disk(key);
    engine ocl_engine = ocl_interop::make_engine(device, context, value);
}
~~~

## Memory descriptor

When serializing primitives, a binary blob can be obtained from a
memory descriptor using @ref dnnl::memory::desc::get_blob. Any binary
blob obtained from @ref dnnl::memory::desc::get_blob can be used to
create a memory descriptor @ref dnnl::memory::desc.

@note
When deserializing a constant tensor, the user must verify that the deserialized memory descriptor matches the memory
descriptor expected by the primitive that will use that memory. The
only circumstance where both are guaranteed to match is when
serialization/deserialization happens on the same system and in the same
environment.



## Limitations

* The primitive and engine APIs are implemented for OpenCL runtime
only. For CPU engine and other runtimes, the library will return
#dnnl_unimplemented (in the case of the C API) or throw a corresponding
@ref dnnl::error exception (in the case of the C++ API).
* Currently, the library cannot differentiate cache blobs created for devices
that have different stepping; therefore, the cache blob can be safely used only
on the system where it is created.