feat: Add minimal transformer block with multi-head attention - #10
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Pre-LN encoder block: multi-head self-attention, position-wise GELU FFN, residual connections, and LayerNorm. Numpy only, with shape and mask tests.
Document transformer as forward-only (no optimizer/grad claim). Pin scaled_dot_product_attention to softmax(QK^T/sqrt(d_k))V with a scale-sensitive golden test. Softmax all-(-inf) rows return zeros without RuntimeWarning/nan; fully-masked attention covered in tests.
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Add a forward-only Pre-LN self-attention block (multi-head scaled dot-product attention, position-wise GELU FFN, residual + LayerNorm) with optional causal masking.
No backward/grad path yet:
parameters()exposes weight tensors for inspection only, not for the MLP optimizers. Attention is checked against a goldensoftmax(QKᵀ/√d_k)Vreference (scale load-bearing). All-masked (-inf) softmax rows return zeros without RuntimeWarning/nan.