Add offload funcs
This commit is contained in:
Phillip Kravtsov
parent
d904aff040
commit
ec0ce978ff
1 changed files with 159 additions and 74 deletions
233
llama.cpp
233
llama.cpp
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@ -4032,17 +4032,19 @@ static struct ggml_cgraph * llm_build_persimmon(
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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 size_t n_rot = n_embd_head / 2;
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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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float norm_eps = 1e-5f;//: hparams.f_norm_eps;
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LLAMA_LOG_INFO("norm_eps: %f\n", 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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const bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift;
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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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struct ggml_init_params params = {
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@ -4066,6 +4068,11 @@ 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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LLAMA_LOG_INFO("Input tokens are: [");
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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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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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@ -4074,6 +4081,9 @@ static struct ggml_cgraph * llm_build_persimmon(
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memcpy(inpL->data, batch.embd, n_tokens * n_embd * ggml_element_size(inpL));
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}
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}
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offload_func_t offload_func_nr = llama_nop; // nr = non-repeating
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offload_func_t offload_func_kq = llama_nop;
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offload_func_t offload_func_v = llama_nop;
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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_allocr_alloc(lctx.alloc, KQ_scale);
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@ -4082,8 +4092,10 @@ static struct ggml_cgraph * llm_build_persimmon(
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}
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ggml_set_name(KQ_scale, "1/sqrt(n_embd_head)");
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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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offload_func_kq(KQ_mask);
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ggml_set_name(KQ_mask, "KQ_mask");
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ggml_allocr_alloc(lctx.alloc, KQ_mask);
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if (!ggml_allocr_is_measure(lctx.alloc)) {
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float * data = (float *) KQ_mask->data;
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memset(data, 0, ggml_nbytes(KQ_mask));
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@ -4101,6 +4113,7 @@ static struct ggml_cgraph * llm_build_persimmon(
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}
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struct ggml_tensor * KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
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offload_func_kq(KQ_pos);
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ggml_set_name(KQ_pos, "KQ_pos");
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ggml_allocr_alloc(lctx.alloc, KQ_pos);
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if (!ggml_allocr_is_measure(lctx.alloc)) {
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@ -4110,8 +4123,8 @@ static struct ggml_cgraph * llm_build_persimmon(
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}
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}
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if (do_rope_shift) {
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LLAMA_LOG_INFO("do_rope_shift...?\n");
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struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
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offload_func_kq(K_shift);
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ggml_set_name(K_shift, "K_shift");
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ggml_allocr_alloc(lctx.alloc, K_shift);
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if (!ggml_allocr_is_measure(lctx.alloc)) {
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@ -4122,154 +4135,195 @@ static struct ggml_cgraph * llm_build_persimmon(
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}
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * tmp =
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// we rotate only the first n_rot dimensions.
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ggml_rope_custom_inplace(ctx0,
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ggml_view_3d(ctx0, kv_self.k,
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n_rot, n_head, n_ctx,
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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_head*n_ctx*il)// + n_rot)
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ggml_element_size(kv_self.k)*(n_embd_head*n_ctx*il)
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),
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K_shift, n_rot, 2, 0, freq_base, freq_scale);
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offload_func_kq(tmp);
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ggml_build_forward_expand(gf, tmp);
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}
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}
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for (int il=0; il < n_layer; ++il) {
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struct ggml_tensor * residual = ggml_dup(ctx0, inpL);
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struct ggml_tensor * residual = inpL;
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offload_func_t offload_func = llama_nop;
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{
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cur = ggml_norm(ctx0, inpL, norm_eps);
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offload_func(cur);
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cur = ggml_mul(ctx0, cur, model.layers[il].attn_norm);
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offload_func(cur);
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cur = ggml_add(ctx0, cur, model.layers[il].attn_norm_b);
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offload_func(cur);
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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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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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offload_func_kq(cur);
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cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
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offload_func_kq(cur);
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// split qkv
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GGML_ASSERT(n_head_kv == n_head);
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ggml_set_name(cur, format("qkv_%d", il).c_str());
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struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens);
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// get it to (d_h, n_head, L, 3)
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offload_func_kq(tmpqkv);
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struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2));
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offload_func_kq(tmpqkv_perm);
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ggml_format_name(tmpqkv_perm, "tmpqkv_perm_%d", il);
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struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_3d(
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struct ggml_tensor * tmpq = ggml_view_3d(
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ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
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ggml_element_size(tmpqkv_perm) * n_embd_head,
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ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
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0
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));
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);
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offload_func_kq(tmpq);
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struct ggml_tensor * tmpk = ggml_view_3d(
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ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
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ggml_element_size(tmpqkv_perm) * n_embd_head,
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ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
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ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens
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);
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struct ggml_tensor * tmpv = ggml_cont(ctx0, ggml_view_3d(
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ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
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ggml_element_size(tmpqkv_perm) * n_embd_head,
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ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
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ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2
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));
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offload_func_kq(tmpk);
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// Q/K Layernorm
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tmpq = ggml_norm(ctx0, tmpq, norm_eps);
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offload_func_kq(tmpq);
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tmpq = ggml_mul(ctx0, tmpq, model.layers[il].attn_q_norm);
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offload_func_kq(tmpq);
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tmpq = ggml_add(ctx0, tmpq, model.layers[il].attn_q_norm_b);
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offload_func_kq(tmpq);
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tmpk = ggml_norm(ctx0, tmpk, norm_eps);
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offload_func_v(tmpk);
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tmpk = ggml_mul(ctx0, tmpk, model.layers[il].attn_k_norm);
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offload_func_v(tmpk);
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tmpk = ggml_add(ctx0, tmpk, model.layers[il].attn_k_norm_b);
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offload_func_v(tmpk);
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struct ggml_tensor * qrot = ggml_cont(ctx0, ggml_view_3d(
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// RoPE the first n_rot of q/k, pass the other half, and concat.
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struct ggml_tensor * qrot = ggml_view_3d(
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ctx0, tmpq, n_rot, n_head, n_tokens,
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ggml_element_size(tmpq) * n_embd_head,
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ggml_element_size(tmpq) * n_embd_head * n_head,
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0
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));
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struct ggml_tensor * krottmp = ggml_view_3d(
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ctx0, tmpk, n_rot, n_head, n_tokens,
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/* nb1 = */ ggml_element_size(tmpk) * n_embd_head,
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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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struct ggml_tensor * krot = ggml_cont(ctx0, krottmp);
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offload_func_kq(qrot);
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ggml_format_name(qrot, "qrot_%d", il);
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struct ggml_tensor * krot = ggml_view_3d(
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ctx0, tmpk, n_rot, n_head, n_tokens,
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ggml_element_size(tmpk) * n_embd_head,
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ggml_element_size(tmpk) * n_embd_head * n_head,
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0
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);
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offload_func_kq(krot);
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ggml_format_name(krot, "krot_%d", il);
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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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struct ggml_tensor * qpass = ggml_view_3d(
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ctx0, tmpq, n_rot, n_head, n_tokens,
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ggml_element_size(tmpq) * n_embd_head,
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ggml_element_size(tmpq) * n_embd_head * n_head,
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ggml_element_size(tmpq) * n_rot
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));
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struct ggml_tensor * kpass = ggml_cont(ctx0, ggml_view_3d(
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);
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offload_func_kq(qpass);
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ggml_format_name(qpass, "qpass_%d", il);
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struct ggml_tensor * kpass = ggml_view_3d(
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ctx0, tmpk, n_rot, n_head, n_tokens,
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ggml_element_size(tmpk) * n_embd_head,
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ggml_element_size(tmpk) * n_embd_head * n_head,
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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(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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);
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offload_func_kq(kpass);
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ggml_format_name(kpass, "kpass_%d", il);
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struct ggml_tensor * qrotated = ggml_cont(ctx0, ggml_permute(ctx0,
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ggml_rope_custom(
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struct ggml_tensor * qrotated = ggml_rope_custom(
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ctx0, qrot, KQ_pos, n_rot, 2, 0, freq_base, freq_scale
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),
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2, 1, 0, 3
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));
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qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
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struct ggml_tensor * krotated = ggml_cont(ctx0, ggml_permute(ctx0,
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ggml_rope_custom(
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);
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offload_func_kq(qrotated);
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struct ggml_tensor * krotated = ggml_rope_custom(
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ctx0, krot, KQ_pos, n_rot, 2, 0, freq_base, freq_scale
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),
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2, 1, 0, 3
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));
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kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
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);
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offload_func_kq(krotated);
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// ggml currently only supports concatenation on dim=2
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// so we need to permute qrot, qpass, concat, then permute back.
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qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3));
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offload_func_kq(qrotated);
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krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3));
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offload_func_kq(krotated);
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struct ggml_tensor * Qcur = ggml_cont(ctx0,
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ggml_permute(ctx0,
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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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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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ggml_set_name(Kcur, format("Kcur_%d", il).c_str());
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qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
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offload_func_kq(qpass);
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kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
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offload_func_kq(kpass);
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struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass);
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offload_func_kq(Qcur);
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struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
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offload_func_kq(Kcur);
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struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 1, 2, 0, 3));
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offload_func_kq(Q);
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Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
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offload_func_kq(Kcur);
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{
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struct ggml_tensor * tmpv = ggml_view_3d(
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ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens,
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ggml_element_size(tmpqkv_perm) * n_embd_head,
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ggml_element_size(tmpqkv_perm) * n_embd_head * n_head,
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ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2
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);
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offload_func_v(tmpv);
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// store K, V in cache
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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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offload_func_v(Vcur);
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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_gqa,
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(ggml_element_size(kv_self.k)*n_embd_gqa)*(il*n_ctx + kv_head)
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);
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offload_func_kq(k);
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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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offload_func_v(v);
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ggml_set_name(v, "v");
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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 * 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 = 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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offload_func_kq(K);
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ggml_format_name(K, "K_%d", il);
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struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
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offload_func_kq(KQ);
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ggml_set_name(KQ, "KQ");
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struct ggml_tensor * KQ_scaled = ggml_scale(ctx0, KQ, KQ_scale);
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offload_func_kq(KQ_scaled);
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ggml_set_name(KQ_scaled, "KQ_scaled");
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struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
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offload_func_kq(KQ_masked);
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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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offload_func_kq(KQ_soft_max);
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ggml_set_name(KQ_soft_max, "KQ_soft_max");
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struct ggml_tensor * V =
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ggml_view_3d(ctx0, kv_self.v,
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@ -4277,49 +4331,80 @@ static struct ggml_cgraph * llm_build_persimmon(
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|||
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);
|
||||
offload_func_v(V);
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||||
ggml_set_name(V, "V");
|
||||
|
||||
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
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||||
offload_func_v(KQV);
|
||||
ggml_set_name(KQV, "KQV");
|
||||
|
||||
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
||||
offload_func_v(KQV_merged);
|
||||
ggml_set_name(KQV_merged, "KQV_merged");
|
||||
|
||||
cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
|
||||
offload_func_v(cur);
|
||||
ggml_set_name(cur, "KQV_merged_contiguous");
|
||||
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
|
||||
offload_func(cur);
|
||||
cur = ggml_add(ctx0, cur, model.layers[il].bo);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_wo");
|
||||
}
|
||||
cur = ggml_add(ctx0, residual, cur);
|
||||
struct ggml_tensor * residual2 = ggml_dup(ctx0, cur);
|
||||
ggml_set_name(residual2, "residual2");
|
||||
// Norm
|
||||
{
|
||||
cur = ggml_norm(ctx0, cur, norm_eps);
|
||||
|
||||
struct ggml_tensor * inpFF = ggml_add(ctx0, residual, cur);
|
||||
offload_func(inpFF);
|
||||
ggml_set_name(inpFF, "inpFF");
|
||||
{
|
||||
// MLP
|
||||
{
|
||||
// Norm
|
||||
cur = ggml_norm(ctx0, inpFF, norm_eps);
|
||||
offload_func(cur);
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_mul(ctx0, cur, model.layers[il].ffn_norm),
|
||||
model.layers[il].ffn_norm_b
|
||||
);
|
||||
ggml_set_name(cur, "ffn_norm");
|
||||
offload_func(cur);
|
||||
}
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur);
|
||||
offload_func(cur);
|
||||
|
||||
cur = ggml_add(ctx0, cur, model.layers[il].b3);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_ffn_up");
|
||||
|
||||
cur = ggml_sqr(ctx0, ggml_relu(ctx0, cur));
|
||||
ggml_set_name(cur, "result_ffn_act");
|
||||
offload_func(cur);
|
||||
offload_func(cur->src[0]);
|
||||
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur);
|
||||
offload_func(cur);
|
||||
cur = ggml_add(ctx0,
|
||||
ggml_mul(ctx0, cur, model.layers[il].ffn_norm),
|
||||
model.layers[il].ffn_norm_b
|
||||
);
|
||||
cur,
|
||||
model.layers[il].b2);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "outFF");
|
||||
}
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].w3, cur);
|
||||
cur = ggml_add(ctx0, cur, model.layers[il].b3);
|
||||
cur = ggml_sqr(ctx0, ggml_relu(ctx0, cur));
|
||||
cur = ggml_mul_mat(ctx0, model.layers[il].w2, cur);
|
||||
struct ggml_tensor * ffn_out = ggml_add(ctx0,
|
||||
cur,
|
||||
model.layers[il].b2);
|
||||
ggml_format_name(ffn_out, "pre_residual2_%d", il);
|
||||
cur = ggml_add(ctx0, ffn_out, residual2);
|
||||
ggml_set_name(cur, "inpFF_+_attn_out");
|
||||
cur = ggml_add(ctx0, cur, inpFF);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "inpFF_+_outFF");
|
||||
inpL = cur;
|
||||
}
|
||||
cur = inpL;
|
||||
{
|
||||
cur = ggml_norm(ctx0, cur, norm_eps);
|
||||
offload_func_nr(cur);
|
||||
cur = ggml_mul(ctx0, cur, model.output_norm);
|
||||
offload_func_nr(cur);
|
||||
|
||||
ggml_set_name(cur, "printme_final");
|
||||
cur = ggml_add(ctx0, cur, model.output_norm_b);
|
||||
// offload_func_nr(cur);
|
||||
|
||||
ggml_set_name(cur, "result_norm");
|
||||
}
|
||||
cur = ggml_mul_mat(ctx0, model.output, cur);
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue