mirror of
https://github.com/ggerganov/llama.cpp.git
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llama : move refact in correct place + optimize graph input
This commit is contained in:
Georgi Gerganov
parent
739b85c985
commit
da936188d8
584
llama.cpp
584
llama.cpp
@ -3166,10 +3166,10 @@ static struct ggml_cgraph * llm_build_llama(
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ggml_set_name(KQ_pos, "KQ_pos");
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// shift the entire K-cache if needed
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if (do_rope_shift) {
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struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
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ggml_set_name(K_shift, "K_shift");
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if (do_rope_shift) {
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * tmp =
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ggml_rope_custom_inplace(ctx0,
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@ -3440,10 +3440,10 @@ static struct ggml_cgraph * llm_build_baichaun(
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ggml_set_name(KQ_pos, "KQ_pos");
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// shift the entire K-cache if needed
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if (do_rope_shift) {
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struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
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ggml_set_name(K_shift, "K_shift");
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if (do_rope_shift) {
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * tmp =
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ggml_rope_custom_inplace(ctx0,
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@ -3658,247 +3658,6 @@ static struct ggml_cgraph * llm_build_baichaun(
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return gf;
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}
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static struct ggml_cgraph * llm_build_refact(
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llama_context & lctx,
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const llama_batch & batch,
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bool worst_case) {
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const auto & model = lctx.model;
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const auto & hparams = model.hparams;
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const auto & cparams = lctx.cparams;
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const auto & kv_self = lctx.kv_self;
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GGML_ASSERT(!!kv_self.ctx);
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const int64_t n_embd = hparams.n_embd;
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const int64_t n_layer = hparams.n_layer;
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const int64_t n_ctx = cparams.n_ctx;
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const int64_t n_head = hparams.n_head;
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const int64_t n_head_kv = hparams.n_head_kv;
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const int64_t n_embd_head = hparams.n_embd_head();
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const int64_t n_embd_gqa = hparams.n_embd_gqa();
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const float norm_rms_eps = hparams.f_norm_rms_eps;
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const int32_t n_tokens = batch.n_tokens;
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const int32_t n_kv = worst_case ? n_ctx : kv_self.n;
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const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head;
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// printf("n_kv = %d\n", n_kv);
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auto & buf_compute = lctx.buf_compute;
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struct ggml_init_params params = {
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/*.mem_size =*/ buf_compute.size,
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/*.mem_buffer =*/ buf_compute.data,
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/*.no_alloc =*/ true,
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};
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struct ggml_context * ctx0 = ggml_init(params);
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ggml_cgraph * gf = ggml_new_graph(ctx0);
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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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if (batch.token) {
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struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
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ggml_set_name(inp_tokens, "inp_tokens");
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inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
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} else {
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#ifdef GGML_USE_MPI
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GGML_ASSERT(false && "not implemented");
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#endif
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inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
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}
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ggml_set_name(inpL, "inp_embd");
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// KQ_scale
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struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
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ggml_set_name(KQ_scale, "KQ_scale");
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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
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ggml_set_name(KQ_mask, "KQ_mask");
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * inpSA = inpL;
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// norm
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{
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cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps);
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ggml_set_name(cur, "rms_norm_0");
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// cur = cur*attn_norm(broadcasted)
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cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
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ggml_set_name(cur, "attn_norm_0");
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}
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// self-attention
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{
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// compute Q and K
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struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
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ggml_set_name(tmpk, "tmpk");
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struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
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ggml_set_name(tmpq, "tmpq");
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struct ggml_tensor * Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens);
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ggml_set_name(Kcur, "Kcur");
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struct ggml_tensor * Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens);
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ggml_set_name(Qcur, "Qcur");
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// store key and value to memory
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{
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// compute the transposed [n_tokens, n_embd] V matrix
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struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
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ggml_set_name(tmpv, "tmpv");
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struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
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ggml_set_name(Vcur, "Vcur");
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struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
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ggml_set_name(k, "k");
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struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
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( n_ctx)*ggml_element_size(kv_self.v),
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(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
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ggml_set_name(v, "v");
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ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
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ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
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}
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struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
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ggml_set_name(Q, "Q");
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struct ggml_tensor * K =
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ggml_view_3d(ctx0, kv_self.k,
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n_embd_head, n_kv, n_head_kv,
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ggml_element_size(kv_self.k)*n_embd_gqa,
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ggml_element_size(kv_self.k)*n_embd_head,
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ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
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ggml_set_name(K, "K");
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// K * Q
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struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
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ggml_set_name(KQ, "KQ");
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// KQ_scaled = KQ / sqrt(n_embd_head)
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// KQ_scaled shape [n_kv, n_tokens, n_head, 1]
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struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
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ggml_set_name(KQ_scaled, "KQ_scaled");
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// KQ_masked = mask_past(KQ_scaled)
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struct ggml_tensor * KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, /*n_past*/ 0, n_head, 8);
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ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi");
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struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask);
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ggml_set_name(KQ_masked, "KQ_masked");
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// KQ = soft_max(KQ_masked)
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struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
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ggml_set_name(KQ_soft_max, "KQ_soft_max");
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// split cached V into n_head heads
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struct ggml_tensor * V =
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ggml_view_3d(ctx0, kv_self.v,
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n_kv, n_embd_head, n_head_kv,
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ggml_element_size(kv_self.v)*n_ctx,
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ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
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ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
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ggml_set_name(V, "V");
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struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
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ggml_set_name(KQV, "KQV");
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// KQV_merged = KQV.permute(0, 2, 1, 3)
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struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
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ggml_set_name(KQV_merged, "KQV_merged");
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// cur = KQV_merged.contiguous().view(n_embd, n_tokens)
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cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
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ggml_set_name(cur, "KQV_merged_contiguous");
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// projection (no bias)
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cur = ggml_mul_mat(ctx0,
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model.layers[il].wo,
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cur);
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ggml_set_name(cur, "result_wo");
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}
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struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
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ggml_set_name(inpFF, "inpFF");
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// feed-forward network
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{
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// norm
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{
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cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps);
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ggml_set_name(cur, "rms_norm_1");
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// cur = cur*ffn_norm(broadcasted)
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cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
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ggml_set_name(cur, "ffn_norm");
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}
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struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
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model.layers[il].w3,
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cur);
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ggml_set_name(tmp, "result_w3");
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cur = ggml_mul_mat(ctx0,
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model.layers[il].w1,
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cur);
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ggml_set_name(cur, "result_w1");
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// SILU activation
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cur = ggml_silu(ctx0, cur);
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ggml_set_name(cur, "silu");
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cur = ggml_mul(ctx0, cur, tmp);
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ggml_set_name(cur, "silu_x_result_w3");
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cur = ggml_mul_mat(ctx0,
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model.layers[il].w2,
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cur);
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ggml_set_name(cur, "result_w2");
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}
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cur = ggml_add(ctx0, cur, inpFF);
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ggml_set_name(cur, "inpFF_+_result_w2");
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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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// norm
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{
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cur = ggml_rms_norm(ctx0, cur, norm_rms_eps);
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ggml_set_name(cur, "rms_norm_2");
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// cur = cur*norm(broadcasted)
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cur = ggml_mul(ctx0, cur, model.output_norm);
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ggml_set_name(cur, "result_norm");
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}
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// lm_head
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cur = ggml_mul_mat(ctx0, model.output, cur);
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ggml_set_name(cur, "result_output");
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ggml_build_forward_expand(gf, cur);
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ggml_free(ctx0);
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return gf;
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}
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static struct ggml_cgraph * llm_build_falcon(
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llama_context & lctx,
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const llama_batch & batch,
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@ -3976,10 +3735,10 @@ static struct ggml_cgraph * llm_build_falcon(
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ggml_set_name(KQ_pos, "KQ_pos");
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// shift the entire K-cache if needed
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if (do_rope_shift) {
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struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
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ggml_set_name(K_shift, "K_shift");
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if (do_rope_shift) {
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * tmp =
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ggml_rope_custom_inplace(ctx0,
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@ -4774,6 +4533,247 @@ static struct ggml_cgraph * llm_build_persimmon(
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return gf;
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}
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static struct ggml_cgraph * llm_build_refact(
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llama_context & lctx,
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const llama_batch & batch,
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bool worst_case) {
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const auto & model = lctx.model;
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const auto & hparams = model.hparams;
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const auto & cparams = lctx.cparams;
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const auto & kv_self = lctx.kv_self;
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GGML_ASSERT(!!kv_self.ctx);
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const int64_t n_embd = hparams.n_embd;
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const int64_t n_layer = hparams.n_layer;
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const int64_t n_ctx = cparams.n_ctx;
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const int64_t n_head = hparams.n_head;
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const int64_t n_head_kv = hparams.n_head_kv;
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const int64_t n_embd_head = hparams.n_embd_head();
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const int64_t n_embd_gqa = hparams.n_embd_gqa();
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const float norm_rms_eps = hparams.f_norm_rms_eps;
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const int32_t n_tokens = batch.n_tokens;
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const int32_t n_kv = worst_case ? n_ctx : kv_self.n;
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const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head;
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// printf("n_kv = %d\n", n_kv);
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auto & buf_compute = lctx.buf_compute;
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struct ggml_init_params params = {
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/*.mem_size =*/ buf_compute.size,
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/*.mem_buffer =*/ buf_compute.data,
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/*.no_alloc =*/ true,
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};
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struct ggml_context * ctx0 = ggml_init(params);
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ggml_cgraph * gf = ggml_new_graph(ctx0);
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struct ggml_tensor * cur;
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struct ggml_tensor * inpL;
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if (batch.token) {
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struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
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ggml_set_name(inp_tokens, "inp_tokens");
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inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
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} else {
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#ifdef GGML_USE_MPI
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GGML_ASSERT(false && "not implemented");
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#endif
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inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
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}
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ggml_set_name(inpL, "inp_embd");
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// KQ_scale
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struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
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ggml_set_name(KQ_scale, "KQ_scale");
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// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
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struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
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ggml_set_name(KQ_mask, "KQ_mask");
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * inpSA = inpL;
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// norm
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{
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cur = ggml_rms_norm(ctx0, inpL, norm_rms_eps);
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ggml_set_name(cur, "rms_norm_0");
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// cur = cur*attn_norm(broadcasted)
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cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
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ggml_set_name(cur, "attn_norm_0");
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}
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// self-attention
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{
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// compute Q and K
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struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
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ggml_set_name(tmpk, "tmpk");
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struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
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ggml_set_name(tmpq, "tmpq");
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struct ggml_tensor * Kcur = ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens);
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ggml_set_name(Kcur, "Kcur");
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struct ggml_tensor * Qcur = ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens);
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ggml_set_name(Qcur, "Qcur");
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// store key and value to memory
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{
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// compute the transposed [n_tokens, n_embd] V matrix
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struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
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ggml_set_name(tmpv, "tmpv");
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struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
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ggml_set_name(Vcur, "Vcur");
|
||||
|
||||
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_embd_gqa, (ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head));
|
||||
ggml_set_name(k, "k");
|
||||
|
||||
struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_embd_gqa,
|
||||
( n_ctx)*ggml_element_size(kv_self.v),
|
||||
(il*n_ctx)*ggml_element_size(kv_self.v)*n_embd_gqa + kv_head*ggml_element_size(kv_self.v));
|
||||
ggml_set_name(v, "v");
|
||||
|
||||
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
|
||||
ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
|
||||
}
|
||||
|
||||
struct ggml_tensor * Q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
|
||||
ggml_set_name(Q, "Q");
|
||||
|
||||
struct ggml_tensor * K =
|
||||
ggml_view_3d(ctx0, kv_self.k,
|
||||
n_embd_head, n_kv, n_head_kv,
|
||||
ggml_element_size(kv_self.k)*n_embd_gqa,
|
||||
ggml_element_size(kv_self.k)*n_embd_head,
|
||||
ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il);
|
||||
ggml_set_name(K, "K");
|
||||
|
||||
// K * Q
|
||||
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
||||
ggml_set_name(KQ, "KQ");
|
||||
|
||||
// KQ_scaled = KQ / sqrt(n_embd_head)
|
||||
// KQ_scaled shape [n_kv, n_tokens, n_head, 1]
|
||||
struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
|
||||
ggml_set_name(KQ_scaled, "KQ_scaled");
|
||||
|
||||
// KQ_masked = mask_past(KQ_scaled)
|
||||
struct ggml_tensor * KQ_scaled_alibi = ggml_alibi(ctx0, KQ_scaled, /*n_past*/ 0, n_head, 8);
|
||||
ggml_set_name(KQ_scaled_alibi, "KQ_scaled_alibi");
|
||||
|
||||
struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled_alibi, KQ_mask);
|
||||
ggml_set_name(KQ_masked, "KQ_masked");
|
||||
|
||||
// KQ = soft_max(KQ_masked)
|
||||
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
||||
ggml_set_name(KQ_soft_max, "KQ_soft_max");
|
||||
|
||||
// split cached V into n_head heads
|
||||
struct ggml_tensor * V =
|
||||
ggml_view_3d(ctx0, kv_self.v,
|
||||
n_kv, n_embd_head, n_head_kv,
|
||||
ggml_element_size(kv_self.v)*n_ctx,
|
||||
ggml_element_size(kv_self.v)*n_ctx*n_embd_head,
|
||||
ggml_element_size(kv_self.v)*n_ctx*n_embd_gqa*il);
|
||||
ggml_set_name(V, "V");
|
||||
|
||||
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
||||
ggml_set_name(KQV, "KQV");
|
||||
|
||||
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
||||
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
||||
ggml_set_name(KQV_merged, "KQV_merged");
|
||||
|
||||
// cur = KQV_merged.contiguous().view(n_embd, n_tokens)
|
||||
cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
|
||||
ggml_set_name(cur, "KQV_merged_contiguous");
|
||||
|
||||
// projection (no bias)
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].wo,
|
||||
cur);
|
||||
ggml_set_name(cur, "result_wo");
|
||||
}
|
||||
|
||||
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
|
||||
ggml_set_name(inpFF, "inpFF");
|
||||
|
||||
// feed-forward network
|
||||
{
|
||||
// norm
|
||||
{
|
||||
cur = ggml_rms_norm(ctx0, inpFF, norm_rms_eps);
|
||||
ggml_set_name(cur, "rms_norm_1");
|
||||
|
||||
// cur = cur*ffn_norm(broadcasted)
|
||||
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
|
||||
ggml_set_name(cur, "ffn_norm");
|
||||
}
|
||||
|
||||
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
|
||||
model.layers[il].w3,
|
||||
cur);
|
||||
ggml_set_name(tmp, "result_w3");
|
||||
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].w1,
|
||||
cur);
|
||||
ggml_set_name(cur, "result_w1");
|
||||
|
||||
// SILU activation
|
||||
cur = ggml_silu(ctx0, cur);
|
||||
ggml_set_name(cur, "silu");
|
||||
|
||||
cur = ggml_mul(ctx0, cur, tmp);
|
||||
ggml_set_name(cur, "silu_x_result_w3");
|
||||
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].w2,
|
||||
cur);
|
||||
ggml_set_name(cur, "result_w2");
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, inpFF);
|
||||
ggml_set_name(cur, "inpFF_+_result_w2");
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
// norm
|
||||
{
|
||||
cur = ggml_rms_norm(ctx0, cur, norm_rms_eps);
|
||||
ggml_set_name(cur, "rms_norm_2");
|
||||
|
||||
// cur = cur*norm(broadcasted)
|
||||
cur = ggml_mul(ctx0, cur, model.output_norm);
|
||||
ggml_set_name(cur, "result_norm");
|
||||
}
|
||||
|
||||
// lm_head
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
ggml_set_name(cur, "result_output");
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
|
||||
ggml_free(ctx0);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
||||
static struct ggml_cgraph * llm_build_bloom(
|
||||
llama_context & lctx,
|
||||
const llama_batch & batch,
|
||||
@ -5360,7 +5360,7 @@ static void llama_build_graph_input(
|
||||
// inp_tokens
|
||||
if (batch.token) {
|
||||
cur = ggml_graph_get_tensor(graph, "inp_tokens");
|
||||
GGML_ASSERT(cur != nullptr); // required
|
||||
GGML_ASSERT(cur != nullptr && "missing tensor 'inp_tokens'");
|
||||
|
||||
ggml_allocr_alloc(lctx.alloc, cur);
|
||||
|
||||
@ -5374,7 +5374,7 @@ static void llama_build_graph_input(
|
||||
// inp_embd
|
||||
if (batch.embd) {
|
||||
cur = ggml_graph_get_tensor(graph, "inp_embd");
|
||||
GGML_ASSERT(cur != nullptr); // required
|
||||
GGML_ASSERT(cur != nullptr && "missing tensor 'inp_embd'");
|
||||
|
||||
ggml_allocr_alloc(lctx.alloc, cur);
|
||||
|
||||
@ -5386,11 +5386,16 @@ static void llama_build_graph_input(
|
||||
}
|
||||
}
|
||||
|
||||
// TODO: make the following required based on the ARCH
|
||||
switch (lctx.model.arch) {
|
||||
case LLM_ARCH_LLAMA:
|
||||
case LLM_ARCH_BAICHUAN:
|
||||
case LLM_ARCH_FALCON:
|
||||
case LLM_ARCH_PERSIMMON:
|
||||
{
|
||||
// KQ_pos
|
||||
cur = ggml_graph_get_tensor(graph, "KQ_pos");
|
||||
GGML_ASSERT(cur != nullptr && "missing tensor 'KQ_pos'");
|
||||
|
||||
// inp_pos
|
||||
cur = ggml_graph_get_tensor(graph, "inp_pos");
|
||||
if (cur) {
|
||||
ggml_allocr_alloc(lctx.alloc, cur);
|
||||
|
||||
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||
@ -5402,22 +5407,63 @@ static void llama_build_graph_input(
|
||||
data[i] = batch.pos[i];
|
||||
}
|
||||
}
|
||||
|
||||
// K_shift
|
||||
cur = ggml_graph_get_tensor(graph, "K_shift");
|
||||
//GGML_ASSERT(cur != nullptr && "missing tensor 'K_shift'");
|
||||
if (cur) {
|
||||
ggml_allocr_alloc(lctx.alloc, cur);
|
||||
|
||||
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||
const int64_t n_ctx = cur->ne[0];
|
||||
|
||||
int32_t * data = (int32_t *) cur->data;
|
||||
|
||||
for (int i = 0; i < n_ctx; ++i) {
|
||||
data[i] = lctx.kv_self.cells[i].delta;
|
||||
}
|
||||
}
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_STARCODER:
|
||||
{
|
||||
// inp_pos
|
||||
cur = ggml_graph_get_tensor(graph, "inp_pos");
|
||||
GGML_ASSERT(cur != nullptr && "missing tensor 'inp_pos'");
|
||||
|
||||
ggml_allocr_alloc(lctx.alloc, cur);
|
||||
|
||||
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||
const int64_t n_tokens = cur->ne[0];
|
||||
|
||||
int32_t * data = (int32_t *) cur->data;
|
||||
|
||||
for (int i = 0; i < n_tokens; ++i) {
|
||||
data[i] = batch.pos[i];
|
||||
}
|
||||
}
|
||||
} break;
|
||||
default:
|
||||
break;
|
||||
}
|
||||
|
||||
// common
|
||||
{
|
||||
// KQ_scale
|
||||
cur = ggml_graph_get_tensor(graph, "KQ_scale");
|
||||
if (cur) {
|
||||
GGML_ASSERT(cur != nullptr && "missing tensor 'KQ_scale'");
|
||||
|
||||
ggml_allocr_alloc(lctx.alloc, cur);
|
||||
|
||||
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||
const int64_t n_embd_head = lctx.model.hparams.n_embd_head();
|
||||
ggml_set_f32(cur, 1.0f/sqrtf(float(n_embd_head)));
|
||||
}
|
||||
}
|
||||
|
||||
// KQ_mask
|
||||
cur = ggml_graph_get_tensor(graph, "KQ_mask");
|
||||
if (cur) {
|
||||
GGML_ASSERT(cur != nullptr && "missing tensor 'KQ_mask'");
|
||||
|
||||
ggml_allocr_alloc(lctx.alloc, cur);
|
||||
|
||||
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||
@ -5441,38 +5487,6 @@ static void llama_build_graph_input(
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// KQ_pos
|
||||
cur = ggml_graph_get_tensor(graph, "KQ_pos");
|
||||
if (cur) {
|
||||
ggml_allocr_alloc(lctx.alloc, cur);
|
||||
|
||||
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||
const int64_t n_tokens = cur->ne[0];
|
||||
|
||||
int32_t * data = (int32_t *) cur->data;
|
||||
|
||||
for (int i = 0; i < n_tokens; ++i) {
|
||||
data[i] = batch.pos[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// K_shift
|
||||
cur = ggml_graph_get_tensor(graph, "K_shift");
|
||||
if (cur) {
|
||||
ggml_allocr_alloc(lctx.alloc, cur);
|
||||
|
||||
if (!ggml_allocr_is_measure(lctx.alloc)) {
|
||||
const int64_t n_ctx = cur->ne[0];
|
||||
|
||||
int32_t * data = (int32_t *) cur->data;
|
||||
|
||||
for (int i = 0; i < n_ctx; ++i) {
|
||||
data[i] = lctx.kv_self.cells[i].delta;
|
||||
}
|
||||
}
|
||||
} while (0);
|
||||
}
|
||||
|
||||
static struct ggml_cgraph * llama_build_graph(
|
||||
|
||||
Loading…
Reference in New Issue
Block a user