Intel® oneAPI Deep Neural Network Developer Guide and Reference
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Visible to Intel only — GUID: GUID-8C968C10-7B11-406E-8571-850A361B4E96
Visible to Intel only — GUID: GUID-8C968C10-7B11-406E-8571-850A361B4E96
Persistent Cache
Creating some oneDNN abstractions can be costly for various reasons. Usually, oneDNN mitigates that overhead by caching such objects but the cache has no effect when the objects are created for the first time. For some applications it can be critical to reduce that overhead.
oneDNN provides an API that can be used to create a persistent cache for such oneDNN abstractions. User can use that API to obtain a cache blob ID and a cache blob to use them as a key and value respectively.
Primitive
The cache blob ID can be obtained via dnnl::enginednnl::primitive_desc_base::get_cache_blob_id
The cache blob can be obtained via 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
In the case 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 from_cache_blob) is part of the verbose output for verbose level 2 (Verbose Mode).
API Usage Example
The following pseudo-code demonstrates a simple example of persistent cache implementation for primitives using the oneDNN API:
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 dnnl::ocl_interop::get_engine_cache_blob_id
The cache blob can obtained via dnnl::ocl_interop::get_engine_cache_blob
Engine can be created with the cache blob via 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:
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); }
Limitations
The API is implemented for the OpenCL runtime only. For CPU engine kind and other runtimes the library will return dnnl_unimplemented in the case of the C API or throw a corresponding dnnl::error exception in the case of the C++ API.
Currently, the library cannot differentiate cache blob created for devices that have different stepping therefore the cache blob can be safely used only on the system where it was created.