remove prints from llama.cpp & fix merge
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c28a6c5ba0
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1 changed files with 6 additions and 96 deletions
102
llama.cpp
102
llama.cpp
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@ -2633,10 +2633,6 @@ static struct ggml_cgraph * llm_build_llama(
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const int32_t n_tokens = batch.n_tokens;
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const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n;
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const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
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LLAMA_LOG_INFO("n_kv = %d\n", n_kv);
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LLAMA_LOG_INFO("n_tokens = %d\n", n_tokens);
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LLAMA_LOG_INFO("n_ctx = %d\n", n_ctx);
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LLAMA_LOG_INFO("kvself.n = %d\n", kv_self.n);
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const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift;
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@ -2875,7 +2871,6 @@ static struct ggml_cgraph * llm_build_llama(
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struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
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offload_func_v(KQ_soft_max);
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ggml_set_name(KQ_soft_max, "KQ_soft_max");
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//ggml_set_name(KQ_soft_max, format("printme_KQ_soft_max_%d", il).c_str());
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// split cached V into n_head heads
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struct ggml_tensor * V =
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@ -4017,19 +4012,6 @@ static struct ggml_cgraph * llm_build_starcoder(
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return gf;
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}
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static void log_tensor(
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ggml_tensor * a
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) {
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LLAMA_LOG_INFO("Shape of %s is ", a->name);
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for (int i = 0; i < a->n_dims; ++i) {
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LLAMA_LOG_INFO("%d", a->ne[i]);
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if (i < a->n_dims - 1) {
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LLAMA_LOG_INFO(",");
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}
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LLAMA_LOG_INFO(" ");
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}
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LLAMA_LOG_INFO("\n");
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}
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static struct ggml_cgraph * llm_build_persimmon(
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llama_context & lctx,
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@ -4042,31 +4024,24 @@ static struct ggml_cgraph * llm_build_persimmon(
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GGML_ASSERT(!!kv_self.ctx);
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const auto & cparams = lctx.cparams;
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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 = hparams.n_ctx;
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const int64_t n_ctx = cparams.n_ctx;
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const int64_t n_head_kv = hparams.n_head_kv;
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const int64_t n_head = hparams.n_head;
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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 freq_base = hparams.rope_freq_base;
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const float freq_scale = hparams.rope_freq_scale;
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const float norm_eps = 1e-5f;
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const float freq_base = cparams.rope_freq_base;
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const float freq_scale = cparams.rope_freq_scale;
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float norm_eps = hparams.f_norm_eps < 0 ? 1e-5f : hparams.f_norm_eps;
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const int32_t n_tokens = batch.n_tokens;
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const int32_t n_kv = ggml_allocr_is_measure(lctx.alloc) ? n_ctx : kv_self.n;
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const int32_t kv_head = ggml_allocr_is_measure(lctx.alloc) ? n_ctx - n_tokens : kv_self.head;
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const size_t n_rot = n_embd_head / 2;
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/*
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printf("\nnorm_eps is %f\n", norm_eps);
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printf("freq_base is %f\n", freq_base);
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LLAMA_LOG_INFO("n_kv = %d\n", n_kv);
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LLAMA_LOG_INFO("n_tokens = %d\n", n_tokens);
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LLAMA_LOG_INFO("n_ctx = %d\n", n_ctx);
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LLAMA_LOG_INFO("kvself.n = %d\n", kv_self.n);
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*/
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const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift;
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auto & buf_compute = lctx.buf_compute;
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@ -4091,13 +4066,6 @@ static struct ggml_cgraph * llm_build_persimmon(
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memcpy(inp_tokens->data, batch.token, n_tokens*ggml_element_size(inp_tokens));
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}
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ggml_set_name(inp_tokens, "inp_tokens");
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/*
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LLAMA_LOG_INFO("\ninp_tokens: [");
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for (int i = 0; i < n_tokens; ++i) {
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LLAMA_LOG_INFO("%d, ", batch.token[i]);
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}
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LLAMA_LOG_INFO("]\n");
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*/
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inpL = ggml_get_rows(ctx0, model.tok_embeddings, inp_tokens);
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} else {
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inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
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@ -4165,21 +4133,16 @@ static struct ggml_cgraph * llm_build_persimmon(
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ggml_build_forward_expand(gf, tmp);
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}
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}
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//LLAMA_LOG_INFO("Entering n_layers loop\n", __func__);
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for (int il=0; il < n_layer; ++il) {
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//ggml_format_name(inpL, "printme_layer_input_%d", il);
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struct ggml_tensor * residual = ggml_dup(ctx0, inpL);
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{
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//ggml_format_name(inpL, "printme_inputs_%d", il);
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cur = ggml_norm(ctx0, inpL, norm_eps);
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cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
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//ggml_format_name(cur, "printme_layernorm_outputs%d", il);
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cur = ggml_add(ctx0, cur, model.layers[il].attn_norm_b);
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ggml_format_name(cur, "input_layernorm_%d", il);
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}
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// self attention
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{
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//log_tensor(cur);
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cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
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ggml_format_name(cur, "qkv_preadd_%d", il);
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cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
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@ -4211,12 +4174,10 @@ static struct ggml_cgraph * llm_build_persimmon(
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));
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tmpq = ggml_norm(ctx0, tmpq, norm_eps);
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tmpq = ggml_mul(ctx0, tmpq, model.layers[il].attn_q_norm);
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//ggml_format_name(tmpq, "printme_tmpq_%d", il);
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tmpq = ggml_add(ctx0, tmpq, model.layers[il].attn_q_norm_b);
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tmpk = ggml_norm(ctx0, tmpk, norm_eps);
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tmpk = ggml_mul(ctx0, tmpk, model.layers[il].attn_k_norm);
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//ggml_format_name(tmpk, "printme_tmpk_%d", il);
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tmpk = ggml_add(ctx0, tmpk, model.layers[il].attn_k_norm_b);
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struct ggml_tensor * qrot = ggml_cont(ctx0, ggml_view_3d(
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@ -4231,7 +4192,6 @@ static struct ggml_cgraph * llm_build_persimmon(
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/* nb2 = */ ggml_element_size(tmpk) * n_embd_head * n_head,
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/* offset = */ 0
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);
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//ggml_format_name(krottmp, "printme_krottmp_%d", il);
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struct ggml_tensor * krot = ggml_cont(ctx0, krottmp);
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// get the second half of tmpq, e.g tmpq[n_rot:, :, :]
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struct ggml_tensor * qpass = ggml_cont(ctx0, ggml_view_3d(
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@ -4247,7 +4207,6 @@ static struct ggml_cgraph * llm_build_persimmon(
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ggml_element_size(tmpk) * n_rot
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));
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ggml_set_name(qrot, format("qrot_%d", il).c_str());
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//ggml_set_name(krot, format("printme_krot_%d", il).c_str());
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ggml_set_name(qpass, format("qpass_%d", il).c_str());
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ggml_set_name(kpass, format("kpass_%d", il).c_str());
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@ -4272,7 +4231,6 @@ static struct ggml_cgraph * llm_build_persimmon(
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ggml_concat(ctx0, qrotated, qpass),
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2, 1, 0, 3));
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struct ggml_tensor * tmp = ggml_permute(ctx0, ggml_concat(ctx0, krotated, kpass), 2, 1, 0, 3);
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//ggml_format_name(tmp, "printme_tmp_%d", il);
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struct ggml_tensor * Kcur = ggml_cont(ctx0, tmp);
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ggml_set_name(Qcur, format("Qcur_%d", il).c_str());
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// kcur appears healthy.
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@ -4295,41 +4253,16 @@ static struct ggml_cgraph * llm_build_persimmon(
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// important: storing RoPE-ed version of K in the KV cache!
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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 * Vcur = ggml_cont(ctx0,
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ggml_transpose(
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ctx0, ggml_reshape_2d(ctx0, ggml_cont(ctx0, tmpv), n_embd, n_tokens)
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));
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ggml_set_name(Vcur, "Vcur");
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struct ggml_tensor * k = ggml_view_1d(
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ctx0, kv_self.k, n_tokens*n_embd,
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(ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + kv_head)
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);
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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,
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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_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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}
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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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//log_tensor(Q);
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// For some reason this is all zeros and no balls...
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struct ggml_tensor * K = 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_format_name(K, "printme_K_%d", il);
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//log_tensor(K);
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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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//ggml_format_name(KQ, "printme_KQ_%d", il);
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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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@ -4337,7 +4270,6 @@ static struct ggml_cgraph * llm_build_persimmon(
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ggml_set_name(KQ_masked, "KQ_masked");
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struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked);
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//ggml_set_name(KQ_soft_max, format("printme_KQ_soft_max_%d", il).c_str());
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struct ggml_tensor * V =
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ggml_view_3d(ctx0, kv_self.v,
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@ -4357,7 +4289,6 @@ static struct ggml_cgraph * llm_build_persimmon(
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ggml_set_name(cur, "KQV_merged_contiguous");
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cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
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//ggml_format_name(cur, "printme_wo_%d", il);
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cur = ggml_add(ctx0, cur, model.layers[il].bo);
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ggml_set_name(cur, "result_wo");
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}
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@ -4376,7 +4307,6 @@ static struct ggml_cgraph * llm_build_persimmon(
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cur = ggml_add(ctx0, cur, model.layers[il].b3);
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cur = ggml_sqr(ctx0, ggml_relu(ctx0, cur));
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cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur);
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//ggml_format_name(cur, "printme_ffn_down_%d", il);
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struct ggml_tensor * ffn_out = ggml_add(ctx0,
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cur,
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model.layers[il].b2);
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@ -4389,7 +4319,6 @@ static struct ggml_cgraph * llm_build_persimmon(
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{
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cur = ggml_norm(ctx0, cur, norm_eps);
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cur = ggml_mul(ctx0, cur, model.output_norm);
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//ggml_set_name(cur, "printme_final");
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cur = ggml_add(ctx0, cur, model.output_norm_b);
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ggml_set_name(cur, "result_norm");
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}
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@ -7166,12 +7095,6 @@ struct llama_context * llama_new_context_with_model(
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LLAMA_LOG_INFO("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
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}
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<<<<<<< HEAD
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const auto & hparams = ctx->model.hparams;
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//LLAMA_LOG_INFO("hg\n", __func__);
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=======
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>>>>>>> bc39553c901a91cfcb757863586250838c83eeab
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// resized during inference
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if (params.logits_all) {
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ctx->logits.reserve(cparams.n_ctx*hparams.n_vocab);
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@ -7198,25 +7121,12 @@ struct llama_context * llama_new_context_with_model(
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ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0));
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#ifdef GGML_USE_METAL
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<<<<<<< HEAD
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if (false) {
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if (params.n_gpu_layers > 0) {
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ctx->ctx_metal = ggml_metal_init(1);
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if (!ctx->ctx_metal) {
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LLAMA_LOG_ERROR("%s: ggml_metal_init() failed\n", __func__);
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llama_free(ctx);
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return NULL;
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}
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ggml_metal_graph_find_concurrency(ctx->ctx_metal, gf, false);
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ggml_allocr_set_parse_seq(ctx->alloc, ggml_metal_get_concur_list(ctx->ctx_metal), ggml_metal_if_optimized(ctx->ctx_metal));
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=======
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if (model->n_gpu_layers > 0) {
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ctx->ctx_metal = ggml_metal_init(1);
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if (!ctx->ctx_metal) {
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LLAMA_LOG_ERROR("%s: ggml_metal_init() failed\n", __func__);
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llama_free(ctx);
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return NULL;
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>>>>>>> bc39553c901a91cfcb757863586250838c83eeab
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}
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ggml_metal_log_set_callback(llama_log_callback_default, NULL);
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//ggml_metal_graph_find_concurrency(ctx->ctx_metal, gf, false);
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