
Task: KernelBench Level 3 MinGPTCausalAttention, full forward including QKV and output projections.
Original dimensions: batch 128, sequence 512, embedding 768, 8 heads (head dimension 96); causal mask; dropout probabilities zero.
Use the same get_init_inputs constructor arguments as reference.py. ModelNew.forward receives five tensors:
x [128,512,768], qkv_weight [2304,768], qkv_bias [2304], projection_weight [768,768], projection_bias [768].
Weights use PyTorch Linear's [out_features,in_features] convention. They are fixed parameters from the original seed-42 initialization, passed explicitly and identically to both implementations; do not reinitialize or mutate them. Activations use N(0,1).
The appended reference adapter only supplies these parameter bindings to the original forward. Preserve all projections, bias additions, scaling, causal masking, softmax, head reassembly and output semantics. No precomputed outputs or sibling-run imports.
