Ocannl (neural_nets

module Row = Ocannl_tensor.Row

module Shape = Ocannl_tensor.Shape

module Tensor = Ocannl_tensor.Tensor

module Operation = Ocannl_tensor.Operation

module PrintBox_utils = Ocannl_tensor.PrintBox_utils

module Train : sig ... end

User-facing modules

module Calibrate : sig ... end

Memory-bandwidth calibration (gh-ocannl-578): STREAM-style streaming kernels whose byte counts are exact by construction — one access site per tensor node and direction, no guards — so the calibration rows they append under config autotune_calibration_file satisfy the fitter's per-leg exactness rule and can constrain the envelope's memory leg (model_peak_memory_bandwidth). Matmul-family tuning data cannot: its rows are compute-bound (their bytes-over-time understates achievable bandwidth no matter how exactly counted) and typically carry guards-taken/union upper bounds, so the fit of the memory leg needs exactly this kind of calibration diversity — see tools/calibrate_bandwidth.ml for the command-line entry point and Ir.Cost_model.Calibration for the fit semantics.

module Parallel : sig ... end

module Nn_blocks : sig ... end

module Persistence : sig ... end

module Precision_policy : sig ... end

module Mixed_prec : sig ... end

module Safetensors : sig ... end