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Crate rnn

Crate rnn 

Source

Re-exports§

pub use crate::network::NeuralNetwork;
pub use crate::network::NetworkStats;
pub use crate::network::network_stats;
pub use crate::network::validate_network_parts;
pub use crate::tensor::TensorView;
pub use crate::tensor::tensor_fill;
pub use crate::tensor::tensor_scale_in_place;
pub use crate::tensor::tensor_add_in_place;
pub use crate::scratch::Scratch;
pub use crate::rnn_format::parse_rnn_from_bytes;
pub use crate::rnn_format::RnnHandle;
pub use crate::rnn_format::BlobMeta;
pub use crate::rnn_api::RnnApiError;
pub use crate::rnn_api::rnn_required_dense_from_topology;
pub use crate::rnn_api::rnn_required_dense_from_bytes_v1;
pub use crate::rnn_api::rnn_dense_required_buffers;
pub use crate::rnn_api::rnn_dense_required_infer_scratch_from_specs;
pub use crate::rnn_api::rnn_validate_dense_topology;
pub use crate::rnn_api::rnn_validate_dense_counts;
pub use crate::rnn_api::rnn_pack_dense_v1;
pub use crate::rnn_api::rnn_unpack_dense_v1;
pub use crate::rnn_api::rnn_run_dense_v1;
pub use crate::crypto::Sha256Ctx;
pub use crate::crypto::Sha512Ctx;
pub use crate::crypto::sha256_bytes;
pub use crate::crypto::sha512_bytes;
pub use crate::crypto::digest_to_hex_lower;
pub use crate::crypto::constant_time_eq;
pub use crate::crypto::verify_sha256;
pub use crate::crypto::verify_sha512;
pub use crate::conv3d::conv3d_forward;
pub use crate::conv3d::conv3d_layout_compatible;
pub use crate::conv3d::conv3d_is_compatible;
pub use crate::conv5d::conv5d_forward;
pub use crate::conv5d::conv5d_backward;
pub use crate::sphere5d::Sphere5D;
pub use crate::sphere5d::NeuronPoint;
pub use crate::sphere5d::SphereError;
pub use crate::activations::ActivationKind;
pub use crate::layers::LayerSpec;
pub use crate::layers::DenseLayerDesc;
pub use crate::layers::LayerPlan;
pub use crate::layers::LayerError;
pub use crate::engine::forward_dense_plan;
pub use crate::engine::forward_dense_plan_big_kernel;
pub use crate::engine::required_batch_scratch_len;
pub use crate::engine::ForwardError;
pub use crate::engine::required_single_infer_scratch;
pub use crate::engine::validate_forward_io;
pub use crate::model_format::encode_dense_model_v1;
pub use crate::model_format::decode_dense_model_v1;
pub use crate::model_format::encoded_size_v1;
pub use crate::model_format::DecodedCounts;
pub use crate::model_format::ModelFormatError;
pub use crate::losses::LossKind;
pub use crate::losses::LossError;
pub use crate::losses::loss_and_gradient;
pub use crate::losses::reduce_sum;
pub use crate::losses::reduce_mean;
pub use crate::metrics::MetricError;
pub use crate::metrics::mse;
pub use crate::metrics::mae;
pub use crate::metrics::argmax;
pub use crate::metrics::accuracy_top1_from_one_hot;
pub use crate::metrics::cross_entropy_from_probabilities;
pub use crate::metrics::RunningMean;
pub use crate::initializers::InitKind;
pub use crate::initializers::InitError;
pub use crate::initializers::expected_parameter_counts;
pub use crate::initializers::initialize_dense_parameters;
pub use crate::inference::InferenceError;
pub use crate::inference::softmax_stable;
pub use crate::inference::forward_dense_batch;
pub use crate::inference::normalize_logits_in_place;
pub use crate::inference::argmax_index;
pub use crate::trainer::DenseSgdConfig;
pub use crate::trainer::TrainError;
pub use crate::trainer::required_train_buffer_len;
pub use crate::trainer::dense_sgd_step;
pub use crate::optimizers::OptimizerKind;
pub use crate::optimizers::OptimizerError;
pub use crate::optimizers::optimizer_state_len;
pub use crate::optimizers::apply_optimizer_step;
pub use crate::schedulers::LrSchedule;
pub use crate::schedulers::ScheduleError;
pub use crate::schedulers::compute_learning_rate;
pub use crate::normalization::NormError;
pub use crate::normalization::layer_norm_in_place;
pub use crate::normalization::layer_norm;
pub use crate::normalization::rms_norm_in_place;
pub use crate::normalization::rms_norm;
pub use crate::attention::AttentionError;
pub use crate::attention::AttentionMask;
pub use crate::attention::AttentionShape;
pub use crate::attention::scaled_dot_product_attention;
pub use crate::quantization::QuantError;
pub use crate::quantization::quantize_i8_symmetric;
pub use crate::quantization::dequantize_i8_symmetric;
pub use crate::quantization::matmul_i8_f32;
pub use crate::model_config::TransformerConfig;
pub use crate::model_config::ConfigError;
pub use crate::model_config::tiny_transformer;
pub use crate::model_config::small_transformer;
pub use crate::model_config::base_transformer;
pub use crate::runtime::RuntimeProfile;
pub use crate::runtime::RuntimeEstimate;
pub use crate::runtime::RuntimeError;
pub use crate::runtime::RuntimeFlopsEstimate;
pub use crate::runtime::ThroughputEstimate;
pub use crate::runtime::BudgetFit;
pub use crate::runtime::estimate_runtime_memory;
pub use crate::runtime::estimate_runtime_flops;
pub use crate::runtime::estimate_tokens_per_second;
pub use crate::runtime::check_runtime_budget;
pub use crate::runtime::fit_from_estimate;
pub use crate::sampling::SamplingError;
pub use crate::sampling::softmax_temperature;
pub use crate::sampling::argmax_sample;
pub use crate::sampling::sample_from_cumulative;
pub use crate::sampling::top_k_mask;
pub use crate::sampling::top_p_cutoff;
pub use crate::kv_cache::KvCacheError;
pub use crate::kv_cache::KvCacheView;
pub use crate::rope::RopeError;
pub use crate::rope::apply_rope_in_place;
pub use crate::embeddings::EmbeddingError;
pub use crate::embeddings::gather_embeddings;
pub use crate::embeddings::tied_output_projection;
pub use crate::lora::LoraError;
pub use crate::lora::apply_lora_delta;
pub use crate::moe::MoeError;
pub use crate::moe::top1_gating;
pub use crate::moe::route_top1;
pub use crate::beam_search::BeamError;
pub use crate::beam_search::select_top_beams;
pub use crate::gradients::GradientError;
pub use crate::gradients::l2_norm;
pub use crate::gradients::clip_by_global_norm;
pub use crate::gradients::all_finite;
pub use crate::batching::BatchError;
pub use crate::batching::pad_sequences_u32;
pub use crate::batching::make_padding_mask;
pub use crate::profiler::OpCounter;

Modules§

activations
attention
batching
beam_search
conv3d
conv5d
crypto
embeddings
engine
gradients
inference
initializers
kv_cache
layers
lora
losses
math
metrics
model_config
model_format
moe
network
normalization
optimizers
profiler
quantization
rnn_api
rnn_format
rope
runtime
sampling
schedulers
scratch
sphere5d
tensor
trainer