============================== Release Notes: v0.104 ==============================

C++ API:

Support for new training algorithms:

Support for new network structures:
 - Added GPT-3 transformers and training recipes

Support for new layers:
 - Select operator (set tensor value based on predicate)
 - Model parallelism for channel-wise fully-connected layers

Python front-end:
 - Support for PyTorch Module conversion to LBANN graphs (requires PyTorch 2.0
   or newer, compiled with PyTorch Dynamo)

Performance optimizations:
 - Support in-place computations for capable layers as a memory optimization
 - Allow distconv-enabled convolution and batchnorm layers to reuse their
   input activations as error signals as a memory optimization if the parent
   layer does not need its activations in the backward pass. This optimization
   can be disabled by setting the environment variable
   DISTCONV_DISABLE_MEM_OPT=1.
 - Added support for selective weight sharding (also known as
   Fully-Sharded Data Parallelism, or FSDP). To enable, set sharded=true
   on weight objects.
 - Allow distconv to be disabled at runtime with LBANN_DISABLE_DISTCONV=1.
 - Activations are now deallocated when no longer needed via a reference counter,
   disable with LBANN_DISABLE_ACT_GC=1.
 - Added option for LBANN to set the number of OMP threads to modest
   default (4) if the environment doesn't specify anything.
 - Save memory on backpropagation by not replicating gradients between
   GradientManager and data_type_optimizer
 - Save more memory in FSDP by synchronizing previous outstanding
   async communication calls and freeing up local gradient contributions
 - FSDP: release full weight views after backprop
 - Batching heads in multi-head attention into single operations
   instead of on a per-head basis
 - Stacking the weights and biases for queries/keys/values in
   self-attention

Model portability & usability:
 - Added support for profiling with Caliper

Experiments & Applications:
 - Updated CosmoFlow model to automatically scale the model
   architecture and parallelism with input size.
 - Added a PyTorch reference implementation of CosmoFlow.

Internal features:
 - Removed the mini_batch_size parameter from the following functions
   in the layer class hierarchy: fp_setup_inputs, fp_setup_outputs, bp_setup_gradient_wrt_inputs
   and the distconv_adapter class: fp_setup, bp_setup
 - Support global and local gradient norm clipping with the clip_gradient_norm callback
 - Interactive progress bar with the progress_bar callback
 - Evaluate progress callback allows for periodic monitoring during
   training with independent data set (intra-epoch evaluation)
 - Detailed memory usage profiling with the memory_profiler callback
 - Refactored subgraph parallelism

I/O & data readers:
 - Renamed percent_of_data_to_use more accurately to fraction_of_data_to_use.
 - DataReaderMetaData, training_dr_linearized_data_size, and num_parallel_readers
   were removed from the model and layer API, and instead reside in the data
   ingestion pipeline.
 - Fixed implementation of background I/O to achive better decoupling
   of background data fetch. Can be enabled / disabled with runtime
   flag.
 - Set the default number of I/O threads to 4
 - Changed the I/O and transform pipeline to use a bank of RNGs that
   is now indexed by the sample ID in the load sequence, rather than the
   I/O thread ID.  This eliminates variablility when using different
   numbers of I/O threads.
 - Moved state tracking current position in a data set from the data
   reader to the dataset class.
 - Split the I/O RNGs into two banks one for training and one for all
   other execution modes.

Build system:
 - Updated build script to use CachedCMakeProject mode, which should
   simplfy the overall workflow
 - Set a default time limit for CI tests to avoid unnecessary stalls

Bug fixes:
 - Fixed a bug where in-place layers sometimes attached a locked view
   of a matrix to a mutable view.
 - Fixed a bug when trying to use the legacy HDF5 data reader without data store.
 - Fixed concurrency bugs in the data store
 - Fixed DistConv memory optimization bug

Retired features:
 - Support for autoencoder strategy in the summarize images callback was removed
 - Removed deprecated Layer protobuf fields: weight_data,
   num_neurons_from_data_reader
 - Removed support for calculating a global mini-batch across multiple
   models using the imcomm callback or multiple trainers.  The
   mini-batch is now strictly contained to a single model in a single
   trainer.  This deprecates an unused (and old) multi-model
   execution mode using imcomm callback that predated LTFB.
 - Removed the notion of effective mini-batch size versus current mini-batch size.
 - Remove world master mini-batch adjustment.
 - Remove model offset field.  No longer necessary since data sets do not span models.
 - Remove the cached value of the current mini-batch size from the SGD
   execution context.  It is now only cached in the model.
 - Removed the imcomm "inter-model" callback
 - Removed the num-parallel-readers parameter to the I/O subsystem.
   This eliminates an older version of I/O parallelism that relied on
   a non-data-parallel I/O buffer and had different ranks fetching
   entire mini-batches.  It is superseded by standard data-parallel I/O.

Author: bvanessen

ReleaseNotes.txt

@@ -3,6 +3,33 @@ C++ API:
 
 Support for new training algorithms:
 
+Support for new network structures:
+
+Support for new layers:
+
+Python front-end:
+
+Performance optimizations:
+
+Model portability & usability:
+
+Experiments & Applications:
+
+Internal features:
+
+I/O & data ingestion:
+
+Build system:
+
+Bug fixes:
+
+Retired features:
+
+============================== Release Notes: v0.104 ==============================
+C++ API:
+
+Support for new training algorithms:
+
 Support for new network structures:
  - Added GPT-3 transformers and training recipes