81 lines
3.1 KiB
C++
81 lines
3.1 KiB
C++
#include "../llama-build-context.h"
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#include "../llama-model.h"
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#include "../llama-context.h"
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ggml_cgraph * llm_build_context::build_cohere2() {
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struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, model.max_nodes(n_tokens), false);
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const int64_t n_embd_head = hparams.n_embd_head_v(0);
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k(0));
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const float f_logit_scale = hparams.f_logit_scale;
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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = build_inp_pos();
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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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// cohere2 requires different mask for layers using sliding window (SWA)
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struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
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struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa();
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// sliding window switch pattern
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const int32_t sliding_window_pattern = 4;
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for (int il = 0; il < n_layer; ++il) {
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// three layers sliding window attention (window size 4096) and ROPE
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// fourth layer uses global attention without positional embeddings
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const bool is_sliding = il % sliding_window_pattern < (sliding_window_pattern - 1);
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struct ggml_tensor * KQ_mask_l = is_sliding ? KQ_mask_swa : KQ_mask;
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// self-attention
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auto attn_out = build_std_attention(gf, model.layers[il].attn_norm, inpL, inp_pos, nullptr, nullptr,
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KQ_mask_l, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), 0.f,
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is_sliding ? hparams.n_swa : 0, il, is_sliding, false, true, true);
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cb(attn_out, "attn_out", il);
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if (il == n_layer - 1 && n_tokens > 1) {
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// skip computing output for unused tokens
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struct ggml_tensor * inp_out_ids = build_inp_out_ids();
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attn_out = ggml_get_rows(ctx0, attn_out, inp_out_ids);
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inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
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}
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attn_out->op_params[3] = 1; // i.e., turn off the reduce operation as it is not required
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// feed-forward network
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cur = llm_build_ffn(ctx0, lctx, model.layers[il].attn_norm, inpL, model.layers[il].ffn_up, NULL, NULL, model.layers[il].ffn_gate,
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NULL, NULL, model.layers[il].ffn_down, NULL, NULL, NULL, LLM_FFN_SILU, LLM_FFN_PAR,
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cb, il, gf, false, true, attn_out);
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cb(cur, "ffn_out", il);
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// add together residual + FFN + self-attention
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cur = lctx.cvec.apply_to(ctx0, cur, il);
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cb(cur, "l_out", il);
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// input for next layer
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inpL = cur;
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}
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cur = inpL;
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cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM, cb, -1);
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cb(cur, "result_norm", -1);
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if (f_logit_scale) {
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cur = ggml_scale(ctx0, cur, f_logit_scale);
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cb(cur, "result_norm_scaled", -1);
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}
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// lm_head
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cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
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cb(cur, "result_output", -1);
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ggml_build_forward_expand(gf, cur);
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return gf;
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}
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