aimathkit 0.1.0

Practical AI math equations as usable Python functions.

AI Math Kit

AI Math Kit is a lightweight Python package that turns common AI training, inference, optimization, architecture, quantization, and hardware equations into simple usable NumPy functions. It is designed for learning, prototyping, and understanding the math behind neural networks and Transformers.

Installation

pip install .

Example Usage

from aimathkit import relu, softmax, kv_cache_size, bytes_to_mb

print(relu([-2, -1, 0, 1, 2]))
print(softmax([2.0, 1.0, 0.1]))

cache = kv_cache_size(
    batch=1,
    seq_len=2048,
    layers=24,
    heads=16,
    head_dim=64,
    bytes_per_value=2,
)

print(bytes_to_mb(cache))

Module Overview

• activations.py: ReLU, sigmoid, tanh, GELU, softmax, and activation application.
• layers.py: Linear layers, weighted sums, residuals, and vocabulary logits.
• losses.py: Cross entropy, regression losses, batch loss, and language model loss.
• optimizers.py: Gradient descent, Adam, regularization, dropout, clipping, schedules, and averaging.
• normalization.py: Layer normalization, batch normalization, feature mean, and feature variance.
• attention.py: Query/key/value projections, attention scores, masks, scaled dot-product attention, multi-head helpers, and Transformer block helpers.
• embeddings.py: Embedding lookup, positional embeddings, vector similarity, distances, and retrieval.
• metrics.py: Accuracy, error rate, precision, recall, F1, perplexity, log probabilities, and token/batch counts.
• decoding.py: Temperature scaling, greedy decode, top-k/top-p filtering, renormalization, and repetition penalty.
• vision.py: Valid convolution, pooling, patch embeddings, and pixel normalization.
• reinforcement.py: Reward sums, discounted returns, value/Q updates, advantages, and policy loss.
• quantization.py: Affine and symmetric quantization, integer dot product, LoRA, sparsity, pruning, and compression.
• hardware.py: KV cache, activation memory, FLOPs, training compute, parameter memory, throughput, latency, bandwidth, utilization, and cost.
• design.py: Parameter counts for linear layers, attention, MLPs, Transformer blocks, and full Transformers.
• utils.py: Byte conversions, safe division, and NumPy conversion.

Function List

Activations

apply_activation, relu, sigmoid, tanh_activation, gelu, softmax

Layers

linear_layer, matrix_multiply_layer, weighted_sum, residual_add, logits_from_hidden

Losses

cross_entropy, mean_squared_error, mean_absolute_error, mini_batch_loss, language_model_loss, scale_loss

Optimizers

gradient_descent_update, weight_update, bias_update, average_gradient, momentum_update, adam_first_moment, adam_second_moment, adam_update, weight_decay, l2_regularization_loss, l1_regularization_loss, dropout_mask, apply_dropout, scaled_dropout, clip_gradient, learning_rate_warmup, linear_lr_decay, cosine_lr_decay, exponential_moving_average, running_average_loss, unscale_gradient, cast_precision, parameter_average, accumulate_gradients, average_accumulated_gradient

Normalization

layer_norm, feature_mean, feature_variance, batch_norm

Attention

query_projection, key_projection, value_projection, attention_scores, scaled_attention_scores, attention_weights, attention_output, scaled_dot_product_attention, causal_mask, apply_attention_mask, multi_head_attention_split, multi_head_concat, feed_forward_network, pre_norm_attention_block, next_token_probability

Embeddings

embedding_lookup, add_positional_embeddings, dot_similarity, cosine_similarity, vector_norm, normalize_vector, euclidean_distance, squared_distance, retrieval_score, top_k_retrieval

Metrics

accuracy, error_rate, precision, recall, f1_score, perplexity, sequence_log_probability, average_log_probability, tokens_per_batch, steps_per_epoch, total_tokens, effective_batch_size

Decoding

temperature_scale_logits, greedy_decode, top_k_filter, top_p_filter, renormalize_probs, repetition_penalty

Vision

conv2d_single_channel, conv2d_multi_channel, max_pool2d, avg_pool2d, image_patch_embedding, pixel_normalize

Reinforcement

reward_sum, discounted_return, value_update, q_update, advantage, policy_probability, policy_loss

Design

linear_layer_params, attention_params, mlp_params, transformer_block_params, transformer_params

Hardware

kv_cache_size, activation_memory, matmul_flops, training_compute_estimate, parameter_memory, tokens_per_second, latency_per_token, utilization, memory_bandwidth_time, arithmetic_intensity, cache_hit_rate, cache_miss_rate, tokens_per_dollar

Quantization

quantize_affine, dequantize_affine, quantize_symmetric, quantization_scale, int8_dot_product, low_rank_factorization_reconstruct, lora_update, apply_sparsity_mask, prune_small_weights, compression_ratio

Utils

bytes_to_kb, bytes_to_mb, bytes_to_gb, safe_divide, ensure_numpy

Educational Note

AI Math Kit is educational and practical for small prototypes. It is not meant to replace NumPy, PyTorch, TensorFlow, JAX, or other production machine-learning frameworks.

License

MIT License. See LICENSE for details.