Why not use PyTorch’s cpp_extension?

[Lenan22]

`
JointTransformerBlock::JointTransformerBlock(int dim, int num_attention_heads, int attention_head_dim, bool context_pre_only, Tensor::ScalarType dtype, Device device) :
dim(dim),
dim_head(attention_head_dim / num_attention_heads),
num_heads(num_attention_heads),
context_pre_only(context_pre_only),
norm1(dim, false, dtype, device),
norm1_context(dim, context_pre_only, dtype, device),
qkv_proj(dim, dim * 3, true, dtype, device),
qkv_proj_context(dim, dim * 3, true, dtype, device),
norm_q(dim_head, 1e-6, false, dtype, device),
norm_k(dim_head, 1e-6, false, dtype, device),
norm_added_q(dim_head, 1e-6, false, dtype, device),
norm_added_k(dim_head, 1e-6, false, dtype, device),
attn(num_attention_heads, attention_head_dim / num_attention_heads, device),
out_proj(dim, dim, true, dtype, device),
out_proj_context(dim, dim, true, dtype, device),
norm2(dim, 1e-6, false, dtype, device),
norm2_context(dim, 1e-6, false, dtype, device),
mlp_fc1(dim, dim * 4, true, dtype, device),
mlp_fc2(dim * 4, dim, true, dtype, device),
mlp_context_fc1(dim, dim * 4, true, dtype, device),
mlp_context_fc2(dim * 4, dim, true, dtype, device)
{
registerChildren
(norm1, "norm1")
(norm1_context, "norm1_context")
(qkv_proj, "qkv_proj")
(qkv_proj_context, "qkv_proj_context")
(norm_q, "norm_q")
(norm_k, "norm_k")
(norm_added_q, "norm_added_q")
(norm_added_k, "norm_added_k")
(attn, "attn")
(out_proj, "out_proj")
(out_proj_context, "out_proj_context")
(norm2, "norm2")
(norm2_context, "norm2_context")
(mlp_fc1, "mlp_fc1")
(mlp_fc2, "mlp_fc2")
(mlp_context_fc1, "mlp_context_fc1")
(mlp_context_fc2, "mlp_context_fc2")
;
}

`

I understand that CUDA operators can be directly encapsulated into PyTorch’s cpp_extension for implementation and calling. Why would one need to rebuild the entire network using C++?