CHANGES.md

[0.5.0] -- current

Added

• Interface files for Backends and Low_level.
• Fixed #245: tracking of used memory. But there's room for improvement.
• Stream-to-stream synchronization functionality, with lazy per-tensor-node synchronization.

Changed

• Migrated to cudajit 0.6.1.
• Verifying that code is linked with the right contexts, by tracking embedded_nodes with assignments.
• Renaming: (virtual) device -> stream, physical_device -> device.
• New files: split out backend_intf.ml, backend_impl.ml, schedulers.ml from backends.ml; moved Tnode.task to task.ml; renamed backend_utils.ml to c_syntax.ml.
• Removed half-static verification of merge buffer nodes inside device_to_device.
• Fixed #286: cross-stream-sharing incorporated into Tnode.memory_mode.
• Moved the multicore backend from a device = stream model to a single device model.
• Got rid of unsafe_cleanup.
• Rename subordinal to stream_id.
• Removed dependency on core, broke up dependency on ppx_jane.
• Huge refactoring of backend internal interfaces and API (not repeating same code).
• Built per-tensor-node stream-to-stream synchronization into copying functions.
• Re-introduced whole-device blocking synchronization, which now is just a slight optimization as it also cleans up event book-keeping.
• Simplifications: no more explicit compilation postponing; no more hard-coded pointers (all non-local arrays are passed by parameter).
• Fresh backends are now fresh modules to structurally prevent any potential cache leaking.

Fixed

• Validating merge nodes for the CUDA backend.
• Checking is_released on weak array retrieval.

[0.4.1] -- 2024-09-17

Added

• Implemented the previously-mocked support for half precision (FP16).
  • We work around the missing Ctypes coverage by not using Ctypes.bigarray_start.
  • We check FP16 constants for overflow.
  • We output half precision specific code from the CUDA backend.
• Finally proper support for mixed precision! Lazy precision defaults and delayed precision setting via Tnode.update_prec.
• A placeholder nn_blocks.ml hinting at an intended design pattern for model components.
• A memory model for the multiple virtual devices per physical device setup, implemented in the CUDA backend. It fixes the CUDA backend behavior in the data parallelism benchmark.
• Slides for the Fun OCaml meetup: docs/Fun OCaml.
• New syntax: inline tensor declarations with a literal float as initial value.

Changed

• Removed the pipes_cc, pipes_gccjit backends (Pipes_multicore_backend) -- I had fixed Pipes_multicore_backend by using the poll library instead of Unix.select, but it turns out to be very very slow.
• Changed the %cd block comment syntax ~~ to allow detailed structuring. Rewrote Train.grad_update to use the %cd syntax.
• Made Train.sgd_one slightly more thrifty: p =- learning_rate *. sgd_delta --> p =- learning_rate * sgd_delta ~logic:"." without the inline tensor expression.

Fixed

• Log levels related de-confusion:
  • Critical bug: logging of computation traces was not properly converted to ppx_minidebug 2.0.
  • Properly restore log_level and inform about its setting.
  • By default do not log from tests.
  • debug_log_from_routines should only happen when log_level > 1.
• Bugs in Multicore_backend: await was not checking queue emptiness, worker's Condition.broadcast was non-atomically guarded (doesn't need to be), possible deadloop due to the lockfree queue -- now replaced with saturn_lockfree.
• Reduced busy-waiting inside c_compile_and_load, propagating compilation errors now instead of infinite loop on error.
• Fixed loss of significant digits for small numbers when outputting files.
• Added missing mixed-precision conversions in the C_syntax backend builder.
• Restored the functionality of debug logging from the cuda backend.
• Always reinitialize global state at the beginning of let%expect_test, to make them more deterministic.

[0.4.0] -- 2024-09-04

Added

• A new backend "cc": C based on a configurable C compiler command, defaulting to cc.
• Merge buffers representational abstraction (one per virtual device):
  • backends just need to support device-to-device transfers,
  • merging gets implemented in "user space".
• CUDA streaming multiprocessor parallelism via streams <-> virtual devices.
• Support for cuda-gdb and compute-sanitizer (pass the right arguments to cudajit).
• Inline declarations for (non-differentiable) tensors in the %cd syntax.
• A minimal wrapper Sync_backend creating CPU backends with a single device only, where all calls are synchronous. (It's a baseline and helps debugging.)
• In progress: proper (condition variables based) scheduler. The legacy scheduler (pipes based) kept for now as baseline and to help debugging.
• Documentation for the syntax extensions.
• %op syntax: when under a ~config parameter, refine the inline declared params' labels with config.label.
• %op syntax: incorporate the input tensor's (if any) label in the resulting tensor's label.
• Comments in config files using the line prefix ~~.

Changed

• Terminology in the API: Renamed almost all uses of "jit" into uses of "compile" and / or "link".
• Split the compile-to-ptx phase from the build-module and build-kernel-launcher phase.
• Migrated the CUDA backend to ppx_minidebug-based execution tracing.
• Fixes for mixed precision computations.
• Further terminology refactoring: Renamed Low_level.compile to Low_level.lower;
  • and Low_level.compiled to Low_level.optimized, making it a record.
• Further refactoring of the Backends API:
  • split the device type into virtual device and physical_device,
  • removed the direct support for merge, instead relying on merge buffers.
• Updated to cudajit 0.4.
• A template for C-syntax backends, refactoring CC and CUDA backends.
• Improvements to handling of tensor node labels, and to the Tnode.debug_name function.
• Output files generated by backends, and files generated by logging, in separate subdirectories.
• C-syntax logging: also output the pre-assignment value when logging an assignment.
• Migrated to ppx_minidebug 2.0 with the benefits it brings: no runtime passing, Utils.settings.log_level unified with ppx_minidebug's log levels.

Fixed

• Allow verifying that non-embedded tensor nodes of the tensor(s) associated with a linked code are already in the context passed to link (resp. link_batch), since they won't get introduced into the context. It is the responsibility of helper functions (such as those in Train) to ensure the check.
• Fixed both known and newly discovered shortcomings of the syntax extensions.
• In particular, %op syntax: lift ~config applications out of (tensor) functions.
• Multiple other tiny fixes.

[0.3.3] -- 2024-04-24

Added

• GitHub workflow for continuous integration and API docs.
• Randomness plug-ins via global config randomness_lib: currently only stdlib and for_tests.

Fixed

• A bit of code rot in the Cuda backend mock cuda_backend.missing.ml.
• NPY: Compatibility with OCaml 5.2.0.
• Renamed the main package name from ocannl to neural_nets_lib, to prevent the opam linter from complaining about a confusing name.

[0.3.2] -- 2024-04-22

Added

• let%cd _ = (and let%op _ =?) do not affect root tracking (intended for adding shape constraints).
• More expressive shape constraints: allowing row variables to be sandwiched between leftmost axes beg_dims and rightmost axes dims.
• Einsum notation support for leftmost axes.

Changed

• Cleaned up "user-facing" API by moving IDX and CDSL to Train, and Tensor.O to more precise Operation.At.
• Added interface Tensor.mli to reduce "the user learning surface".
• Improved documentation and layout of Shape.mli.
• A more reasonable syntax for labels specifications and einsum notation. In particular, whitespace insensitive (except whitespace not allowed inside identifiers).
• Vendored the npy package while we wait for a PR.

Fixed

• Moved cudajit to depopts.
• Slice shape inference is now complete, by using leftmost axes beg_dims in constraints.

[0.3.1] -- 2024-04-15

Added

• Tensor parameters saving and restoring, Ndarray saving and restoring.
• An operation outer_sum: like einsum but simpler, addition everywhere.

Changed

• Tweaks to make the project usable as a package (external library).
• Sanitizing code inclusion via code roots management: Tensor.consume_forward_code and consume_backprop_code, (optionally but by default) used from Train.

Fixed

• Shape inference in presence of non-0 fixed indexing inside einsums was broken (because actually not implemented).
• Incompleteness of shape inference for slicing was leading to inferring shapes with no axes: constraint generation was intended to raise a shape error instead. Proper fix coming in 0.3.2 will make slice shape inference complete.

[0.3.0] -- 2024-03-31

Major rewrite. Abandoning the design choices of 0.1 and 0.2.

Added

• Optionally, inferring or checking tensor (batch) sizes from data (e.g. file) sizes.
• Static indexing. A "slice" operator to select individual batches.
• Established the backends API with first-class modules.
• The Train module as an optimization "frontend".
• Parallel optimization across devices.
• Global settings configurable via config files, environment variables, and commandline flags.
• Integration of backend logging with ppx_minidebug (the debug_log_from_routines setting).

Changed

• The Cuda backend is not supported for now. It is (optionally) buildable to reduce code rot.
• Dynamic indexing is not supported anymore (to reduce complexity). It might be reintroduced if needed.
• Factored out the arrayjit library / package containing compilation (former Ndarray, Node, Code).
• Renamed Formula -> Tensor
• No more "form vs. non-form" formulas / tensors.
  • Formula/tensor roots are split into forward roots and backprop roots.
• No more %nn_rs, %nn_dt syntaxes and Synthetic fetch primitive.
• Renamed %nn_op to %op and %nn_cd to %cd.
• Migrated gccjit into a separate repository.
• Migrated cudajit into a separate repository.
• Massive rewrite of shape inference in a declarative style.
• Generalize zero_out to initialize_neutral to prepare arbitrary accumulation operation.
• Renamed Node -> Lazy_array -> Tnode (tensor node).

[0.2.1] -- 2023-07-19

Added

• The Cuda backend.
  • The Cudajit interface based on Nvrtc and the Cuda driver API.
  • A naive Exec_as_cuda backend where the dedicated Task_id axis parallelizes over blocks, and a new dedicated Sample_num axis parallelizes over threads in a block.
  • When outputting debug files, stores the source .cu code and the assembly .ptx code.
  • Supports thread-only tensors, tensors with thread-local "replicated" working copies, constant tensors, and globally updated tensors.
  • The backend uses atomic adds for shared updates, and within-block synchronization to minimize update races and parameter staleness.
  • Debugging: full trace (for thread 0) by logging assignments with the assigned value and indices for the LHS tensor and the RHS tensors, the expression used to compute the assigned value, of values of subexpressions.
• Cuda FFI for retrieving GPU specs and for getting and setting limits.
• Zero_out low-level-code primitive using memset.
• Staged_compilation low-level-code primitive: a (stateful) callback for use by backends.
• When outputting debug files, also stores the high-level code.
• Saving and restoring tensor content to .npz (.npy archive) files (untested).
• Low-level code based optimizations:
  • unrolls ToPowOf with integer exponent,
  • simplifies local computations that are just expressions,
  • some arithmetic simplifications.

Changed

• Monomorphic axis_index, simplified the axes-related types.
• Splits 'a low_level into monomorphic unit_low_level and float_low_level.
• Removes integer bigarray types.
• Refactors Node + NodeUI into Ndarray + Node.
• Tensor printouts include whether a tensor contains NaN or infinity.
• Simplifies the Task_id functionality: removes If_task_id_is and Global Task_id; emoves parallelism from interpret_code; removes task_id_func vs unit_func duplication.

Fixed

• "Non-diff" code inclusion.
• Ensures unique indices/symbols also for the task_id and sample_num bindings.
• Removes endlines from PrintBox_utils benchmark tables cells.

[0.2.0] -- 2023-06-03

Added

• The Gccjit backend operates using "on device" copies of tensors, where the "device memory" is the stack of the C function. This is intended to improve cache locality and reduce cache contention.
  • Three / four synchronization heuristics:
    • "parallel": a slice of the tensor is copied host-to-device at the beginning and device-to-host at the end, without interference because each task has a different slice.
    • "update on host": the tensor is copied host-to-device at the beginning; each write is an update, it reads the old value from host to update it on the host. Thus each write is a synchronization point.
    • "replicated": the tensor is copied host-to-device at the beginning; only task 0 copies device-to-host.
    • "device-only": no copying to/from host.
• On-device-only tensors that are not materialized on the OCaml side.
• A new category of axis dimensions is introduced: Frozen. It is analogous to the Parallel axis category in that a single task execution / "device call" only processes a 1D slice of the axis.
  • Currently, for tensors processed in parallel, we only support processing of a contiguous tensor slice (copied "to device" using memcpy).
• A new syntax %nn_rs ("postprocess results" variant of %nn_dt) for computations that should happen at the end of task execution / refresh step. It's meant to prepare the data to be copied back to the host.

Changed

• Got rid of backend-agnostic synchronization. It was not worth the complexity / implementation effort at this point.
  • Keeping the Rebalance constructor around, but it is not playing any role.
• Got rid of debug_virtual_nodes, was tricky to maintain.
• Dynamic indexing now skips over parallel axes: when there is a Parallel axis on the left, it is preserved in the resulting tensor (slice), and the next-right axis is indexed into instead.
  • Removed the "indexing axes from-right" functionality for now (fails as not implemented).
• Dynamic indexing now can produce virtual nodes.

Fixed

• Dynamic indexing fixes.

[0.1.2] -- 2023-05-12

Added

• Thread-local parameter task_id for automated iteration over a dimension Parallel.
  • This implements multicore SGD.
  • Rebalancing of computations that don't use Parallel, and synchronization in the Gccjit backend, are left as future work.
  • Already provides significant speedups in the interpreter (6-7x for me), but that's a moot point.
  • Giving up further work this approach for now, because the bottleneck is the memory access with Gccjit.
  • Keeping the new representation capability around, maybe it will be a stepping stone to other things.
• Monolithic step update with "macrobatch" (multiple steps within one backend call).

Changed

• Streamlined the source code, e.g. removed the OCaml backend.
• Better syntax for %nn_dt and %nn_op shape specification, allows identifiers.
• Improved virtual node and scalar constant inlining.
• Better debugging, e.g. an option to "trace" Gccjit execution by printing the comments.

[0.1.1] -- 2023-05-06

Added

• An inline constants optimization that compile-time computes scalar constant subexpressions and inlines the values.

Changed

• Improved debuggability.

Fixed

• A last-minute breaking bug (would be nice to have a pre-release or a pre-publish hook to run tests!).
• The virtual nodes optimization is more robust, correct even with aggressive inlining settings (e.g. escaping variables check).

[0.1.0] -- 2023-05-04

Added

• The first changes-tracking release. Earlier development history is still somewhat documented via closed issues.
• Supports single and double precision floats, more precisions in the future.
• Generates a monolithic step update routine executed by refresh_session (), but can generate arbitrary additional routines at arbitrary times to be executed at arbitrary other times within a session.
• An Interpreter backend that can for example log all individual tensor modifications.
• A Gccjit backend that can sometimes be 400x faster than the Interpreter backend (without any debug work/output).
• A virtual nodes (tensors) optimization that inlines computation of a cell in lieu of tensor accesses, can sometimes reduce memory consumption by 1/3.