Stack multiple blocks

The problem, from first principles

[06-assemble-full-block] built ONE complete Transformer block: a self-attention sublayer and a feed-forward sublayer, each wrapped in [01-layer-normalization-forward]'s normalization and [03-residual-connection]'s residual connection. A real Transformer, and every modern LLM, is overwhelmingly just MANY of these identical-in-STRUCTURE (but independently-PARAMETERIZED) blocks, stacked one after another, each block's output feeding directly into the next block's input. This is genuinely most of what "scaling up a model" means in practice: GPT-2 small has 12 blocks, GPT-3 has 96, and the largest modern LLMs stack well over a hundred, each one adding another opportunity for the representation at every position to be further refined against the rest of the sequence.

Each block in the stack has its OWN independently-learned parameters (its own attention weights, its own FFN weights, its own LayerNorm gamma/beta), never shared across blocks, so a 12-block model has 12x as many block-level parameters as a single block, even though every block runs the identical FORWARD computation [06-assemble-full-block] already implements.

From theory to code

Implement stack_transformer_blocks(x, num_heads, blocks_params, mask, eps). blocks_params is a list of dicts, one per block, each containing that block's own set of weights (weight_o, bias_o, ffn_weight1, ffn_bias1, ffn_weight2, ffn_bias2, gamma1, beta1, gamma2, beta2, the exact keyword arguments [06-assemble-full-block]'s transformer_block_forward expects). Run x through each block IN ORDER, each block's output becoming the NEXT block's input.

Constraints

• Blocks run strictly IN ORDER: block i's output is block i+1's input, never the other way around, and never independently against the original x.
• num_heads, mask, and eps are shared across every block in the stack (a real Transformer typically uses the same head count and mask at every layer); only the WEIGHTS differ per block.
• An empty blocks_params list returns x completely unchanged (zero blocks means zero transformation).

Hints

for params in blocks_params:
    x = transformer_block_forward(x, num_heads, mask=mask, eps=eps, **params)
return x