Convolutional Neural Networks

[lukstafi]

This release brings many fixes to affine shape inference and affine projections inference, and for the first time proper padding propagation and initialization of padding margins. The use_padding setup (e.g. valid vs. padded convolution) is now per-affine-operation, configurable from the einsum notation.

The toy end-to-end test for CNNs, "counting circles", shows an interesting failure on what might be just Intel processors? (I can reproduce it on one of my computers.) Might be a quirk but debugging left out of this release.

From the changelog, collected by Claude:

Added

• Neutral element tracking during shape inference for proper padding reset
• use_padding syntax in einsum notation (replacing global flag)
• Circle counting dataset and MLP training test
• Cross-entropy loss and one_hot_of_int_list helper for classification tasks
• out_channels parameter to conv2d for explicit channel specification
• Projection slot detection by naming convention in %cd syntax extension
• Configurable scaling to kaiming and xavier initialization functions
• New documentation: tensors_and_contexts.md, affine indexing for convolutions
• Documentation for op_fun and param_op_fun types, roots, embedded nodes, and params concepts

Changed

• Padding is now reset by tracking neutral elements through shape inference
• Changed default random initialization to uniform1 which doesn't impose shape constraints
• Refactored vbs from Map to list for order-preserving let bindings in syntax extensions
• Infer the shape of inline definitions assigned a slot for %cd expressions with projections in scope

Fixed

• Gracefully disable inlining for convolution patterns
• Don't propagate padding across operations, even if the same tensor participates in them
• Padding margin initialization for tensors with multiple operations
• Padding initialization bug for max-pool operations
• uniform1 periodicity by spreading bits in *_to_uint4x32 conversions
• tropical (max-reduce) backprop to use input-shaped condition tensors
• tropical g2 gradient by using correct projection for kernel gradients
• Kernel extent calculation to depend on kernel size parity
• Shape inference for Total_elems constraints with Strided_var numerators
• compute_row_product to return None for unresolved variables
• Deferred dim variable guessing to Stage 5 for Total_elems propagation
• Padding offset application during lowering for correct buffer indexing
• Intermediate grads from kaiming, xavier
• Random seed initialization missing in transformer test

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This discussion was created from the release Convolutional Neural Networks.