#include "llama_decoder_layer.hpp" #include "infinicore/nn/rmsnorm.hpp" #include "infinicore/ops.hpp" namespace infinilm::models::llama { LlamaDecoderLayer::LlamaDecoderLayer(const LlamaConfig &config, const infinicore::Device &device, size_t layer_idx, infinicore::DataType dtype, engine::distributed::RankInfo rank_info) : layer_idx_(layer_idx) , rank_info_(rank_info){ // Initialize layer normalization layers INFINICORE_NN_MODULE_INIT(input_layernorm, config.hidden_size, config.rms_norm_eps, dtype, device); INFINICORE_NN_MODULE_INIT(post_attention_layernorm, config.hidden_size, config.rms_norm_eps, dtype, device); // Initialize attention and MLP modules INFINICORE_NN_MODULE_INIT(self_attn, config, device, layer_idx, dtype, rank_info_); INFINICORE_NN_MODULE_INIT(mlp, config, device, dtype, rank_info_); } infinicore::Tensor LlamaDecoderLayer::forward(const infinicore::Tensor &hidden_states, const infinicore::Tensor &position_ids, void *kv_cache) const { // Save residual for attention auto residual = hidden_states; // 1. Pre-attention layer normalization auto normed_states = input_layernorm_->forward(hidden_states); // 2. Self-attention with residual connection auto attn_output = self_attn_->forward(normed_states, position_ids, kv_cache); // Add residual: hidden_states = hidden_states + attn_output auto output = infinicore::op::add(residual, attn_output); // Save residual for MLP residual = output; // 3. Post-attention layer normalization normed_states = post_attention_layernorm_->forward(output); // 4. MLP with residual connection auto mlp_output = mlp_->forward(normed_states); // Add residual: output = output + mlp_output output = infinicore::op::add(residual, mlp_output); return output; } } // namespace infinilm::models::llama