⚡ Bolt: Optimize backward pass and accumulate_grad to eliminate redundant clones#129
⚡ Bolt: Optimize backward pass and accumulate_grad to eliminate redundant clones#129teerthsharma wants to merge 1 commit into
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…dant clones In the autograd context, calling `grads[out.index].clone()` inside `backward` triggers unnecessary heap allocations for `Tensor` metadata (shape and stride arrays), even though the underlying data uses `Rc`. By leveraging `Option::take()` to acquire ownership of the gradient, and refactoring `accumulate_grad` to take `grad: Tensor` by value, we can safely compute and insert gradients without cloning. The gradient is then placed back into the Wengert list array (`grads[out.index] = Some(grad);`). This significantly reduces heap fragmentation during hot-loop machine learning backpropagation passes. Co-authored-by: teerthsharma <78080953+teerthsharma@users.noreply.github.com>
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💡 What: Refactored
Context::backwardto useOption::take()to acquire gradient ownership and modifiedContext::accumulate_gradto take the gradientTensorby value instead of by reference.🎯 Why: Autograd backpropagation traverses the graph heavily. Each
clone()of aTensortriggers unnecessary allocations of its internalVec<usize>metadata (shape and strides). Eliminating these clones avoids GC pressure and minimizes heap fragmentation.📊 Impact: Reduces
Tensormetadata allocations significantly per iteration through the backpropagation tape.🔬 Measurement: Verified that the core neural network training tests pass (e.g.,
test_xor_backprop,test_autograd_complex), confirming that topological sort semantics and variable gradient tracking remain strictly correct without borrow checker interference.PR created automatically by Jules for task 13285064063290285247 started by @teerthsharma