Update after #3382
This commit is contained in:
Galunid
parent
8917767f56
commit
c95937642a
1 changed files with 123 additions and 410 deletions
533
llama.cpp
533
llama.cpp
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@ -3080,7 +3080,7 @@ static void llm_load_tensors(
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} break;
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case LLM_ARCH_STABLELM:
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{
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model.tok_embeddings = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
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model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
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// output
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{
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@ -3148,15 +3148,15 @@ static void llm_load_tensors(
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layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
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layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);
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layer.w1 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
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layer.w2 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
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layer.w3 = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
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layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
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layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
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layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
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if (backend == GGML_BACKEND_GPU) {
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vram_weights +=
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ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
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ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
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ggml_nbytes(layer.w1) + ggml_nbytes(layer.w2) + ggml_nbytes(layer.w3);
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ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
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ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
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ggml_nbytes(layer.ffn_gate) + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
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}
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}
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} break;
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@ -4639,6 +4639,119 @@ struct llm_build_context {
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return gf;
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}
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struct ggml_cgraph * build_stablelm() {
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struct 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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inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
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cb(inpL, "inp_embd", -1);
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
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cb(inp_pos, "inp_pos", -1);
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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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cb(KQ_scale, "KQ_scale", -1);
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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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cb(KQ_mask, "KQ_mask", -1);
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// shift the entire K-cache if needed
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if (do_rope_shift) {
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llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, hparams.n_rot, freq_base, freq_scale, cb);
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}
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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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cur = llm_build_norm(ctx0, inpL, hparams,
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model.layers[il].attn_norm,
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model.layers[il].attn_norm_b,
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LLM_NORM, cb, il);
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cb(cur, "attn_norm", il);
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// self-attention
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{
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// compute Q and K and RoPE them
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struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
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cb(Qcur, "Qcur", il);
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struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
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cb(Kcur, "Kcur", il);
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struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
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cb(Vcur, "Vcur", il);
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Qcur = ggml_rope_custom(
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ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
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hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Qcur, "Qcur", il);
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Kcur = ggml_rope_custom(
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ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
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hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
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ext_factor, attn_factor, beta_fast, beta_slow
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);
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cb(Kcur, "Kcur", il);
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llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
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cur = llm_build_kqv(ctx0, hparams, kv_self,
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model.layers[il].wo, NULL,
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Qcur, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
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cb(cur, "kqv_out", il);
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}
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struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
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cb(ffn_inp, "ffn_inp", il);
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// feed-forward network
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{
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cur = llm_build_norm(ctx0, ffn_inp, hparams,
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model.layers[il].ffn_norm,
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model.layers[il].ffn_norm_b,
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LLM_NORM, cb, il);
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cb(cur, "ffn_norm", il);
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cur = llm_build_ffn(ctx0, cur,
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model.layers[il].ffn_up, NULL,
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model.layers[il].ffn_gate, NULL,
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model.layers[il].ffn_down, NULL,
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LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
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cb(cur, "ffn_out", il);
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}
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cur = ggml_add(ctx0, cur, ffn_inp);
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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,
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model.output_norm,
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model.output_norm_b,
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LLM_NORM, cb, -1);
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cb(cur, "result_norm", -1);
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// lm_head
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cur = ggml_mul_mat(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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};
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//
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@ -4810,408 +4923,7 @@ static const std::unordered_map<const char *, llm_offload_func_e> k_offload_map
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static llm_offload_trie k_offload_func_trie(k_offload_map);
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static struct ggml_cgraph * llm_build_stablelm(
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llama_context & lctx,
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const llama_batch & batch) {
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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_rot = hparams.n_rot;
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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 freq_base = cparams.rope_freq_base;
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const float freq_scale = cparams.rope_freq_scale;
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const float norm_eps = hparams.f_norm_eps;
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const int n_gpu_layers = model.n_gpu_layers;
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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 bool do_rope_shift = ggml_allocr_is_measure(lctx.alloc) || kv_self.has_shift;
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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_allocr_alloc(lctx.alloc, inp_tokens);
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if (!ggml_allocr_is_measure(lctx.alloc)) {
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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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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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ggml_allocr_alloc(lctx.alloc, inpL);
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if (!ggml_allocr_is_measure(lctx.alloc)) {
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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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const int i_gpu_start = n_layer - n_gpu_layers;
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(void) i_gpu_start;
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// offload functions set the tensor output backend to GPU
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// tensors are GPU-accelerated if any input or the output has been offloaded
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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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#ifdef GGML_USE_CUBLAS
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if (n_gpu_layers > n_layer) {
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offload_func_nr = ggml_cuda_assign_buffers_no_alloc;
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}
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if (n_gpu_layers > n_layer + 1) {
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offload_func_v = ggml_cuda_assign_buffers_no_alloc;
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}
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if (n_gpu_layers > n_layer + 2) {
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offload_func_kq = ggml_cuda_assign_buffers_no_alloc;
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}
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#endif // GGML_USE_CUBLAS
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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, "1/sqrt(n_embd_head)");
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ggml_allocr_alloc(lctx.alloc, KQ_scale);
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if (!ggml_allocr_is_measure(lctx.alloc)) {
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ggml_set_f32(KQ_scale, 1.0f/sqrtf(float(n_embd_head)));
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}
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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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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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for (int h = 0; h < 1; ++h) {
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for (int j = 0; j < n_tokens; ++j) {
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const llama_pos pos = batch.pos[j];
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const llama_seq_id seq_id = batch.seq_id[j][0];
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for (int i = 0; i < n_kv; ++i) {
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if (!kv_self.cells[i].has_seq_id(seq_id) || kv_self.cells[i].pos > pos) {
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data[h*(n_kv*n_tokens) + j*n_kv + i] = -INFINITY;
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}
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}
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}
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}
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}
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// KQ_pos - contains the positions
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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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int * data = (int *) KQ_pos->data;
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for (int i = 0; i < n_tokens; ++i) {
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data[i] = batch.pos[i];
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}
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}
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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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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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int * data = (int *) K_shift->data;
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for (int i = 0; i < n_ctx; ++i) {
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data[i] = kv_self.cells[i].delta;
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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 * tmp =
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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_embd_head, n_head_kv, n_ctx,
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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,
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ggml_element_size(kv_self.k)*n_embd_gqa*n_ctx*il),
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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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ggml_format_name(inpL, "layer_inp_%d", il);
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offload_func_t offload_func = llama_nop;
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#ifdef GGML_USE_CUBLAS
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if (il >= i_gpu_start) {
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offload_func = ggml_cuda_assign_buffers_no_alloc;
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}
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#endif // GGML_USE_CUBLAS
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struct ggml_tensor * inpSA = inpL;
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// norm
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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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ggml_set_name(cur, "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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offload_func(cur);
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ggml_set_name(cur, "attention_norm_0");
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// add bias
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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_set_name(cur, "attention_norm_0");
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}
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// self-attention
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{
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// compute Q and K and RoPE them
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struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
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offload_func_kq(tmpk);
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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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offload_func_kq(tmpq);
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ggml_set_name(tmpq, "tmpq");
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struct ggml_tensor * Kcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), KQ_pos, n_rot, 2, 0, freq_base, freq_scale);
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offload_func_kq(Kcur);
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ggml_set_name(Kcur, "Kcur");
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struct ggml_tensor * Qcur = ggml_rope_custom(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), KQ_pos, n_rot, 2, 0, freq_base, freq_scale);
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offload_func_kq(Qcur);
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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
|
||||
|
||||
struct ggml_tensor * tmpv = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
|
||||
offload_func_v(tmpv);
|
||||
ggml_set_name(tmpv, "tmpv");
|
||||
|
||||
struct ggml_tensor * Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, tmpv, n_embd_gqa, n_tokens));
|
||||
offload_func_v(Vcur);
|
||||
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));
|
||||
offload_func_kq(k);
|
||||
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));
|
||||
offload_func_v(v);
|
||||
ggml_set_name(v, "v");
|
||||
|
||||
// important: storing RoPE-ed version of K in the KV cache!
|
||||
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);
|
||||
offload_func_kq(Q);
|
||||
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);
|
||||
offload_func_kq(K);
|
||||
ggml_set_name(K, "K");
|
||||
|
||||
// K * Q
|
||||
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
||||
offload_func_kq(KQ);
|
||||
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);
|
||||
offload_func_kq(KQ_scaled);
|
||||
ggml_set_name(KQ_scaled, "KQ_scaled");
|
||||
|
||||
// KQ_masked = mask_past(KQ_scaled)
|
||||
struct ggml_tensor * KQ_masked = ggml_add(ctx0, KQ_scaled, KQ_mask);
|
||||
offload_func_kq(KQ_masked);
|
||||
ggml_set_name(KQ_masked, "KQ_masked");
|
||||
|
||||
// KQ = soft_max(KQ_masked)
|
||||
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
||||
offload_func_v(KQ_soft_max);
|
||||
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);
|
||||
offload_func_v(V);
|
||||
ggml_set_name(V, "V");
|
||||
|
||||
#if 1
|
||||
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);
|
||||
offload_func_v(KQV);
|
||||
ggml_set_name(KQV, "KQV");
|
||||
#else
|
||||
// make V contiguous in memory to speed up the matmul, however we waste time on the copy
|
||||
// on M1 this is faster for the perplexity computation, but ~5% slower for the single-token generation
|
||||
// is there a better way?
|
||||
struct ggml_tensor * V_cont = ggml_cpy(ctx0, V, ggml_new_tensor_3d(ctx0, kv_self.v->type, n_ctx, n_embd_head, n_head));
|
||||
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_cont, KQ_soft_max);
|
||||
#endif
|
||||
|
||||
// KQV_merged = KQV.permute(0, 2, 1, 3)
|
||||
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 = KQV_merged.contiguous().view(n_embd, n_tokens)
|
||||
cur = ggml_cont_2d(ctx0, KQV_merged, n_embd, n_tokens);
|
||||
offload_func_v(cur);
|
||||
ggml_set_name(cur, "KQV_merged_contiguous");
|
||||
|
||||
// projection (no bias)
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].wo,
|
||||
cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_wo");
|
||||
}
|
||||
|
||||
struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA);
|
||||
offload_func(inpFF);
|
||||
ggml_set_name(inpFF, "inpFF");
|
||||
|
||||
// feed-forward network
|
||||
{
|
||||
// norm
|
||||
{
|
||||
cur = ggml_norm(ctx0, inpFF, norm_eps);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "norm_1");
|
||||
|
||||
// cur = cur*ffn_norm(broadcasted)
|
||||
cur = ggml_mul(ctx0, cur, model.layers[il].ffn_norm);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "ffn_norm");
|
||||
|
||||
// add bias
|
||||
// cur = cur*ffn_norm(broadcasted)
|
||||
cur = ggml_add(ctx0, cur, model.layers[il].ffn_norm_b);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "ffn_norm");
|
||||
}
|
||||
|
||||
struct ggml_tensor * tmp = ggml_mul_mat(ctx0,
|
||||
model.layers[il].w3,
|
||||
cur);
|
||||
offload_func(tmp);
|
||||
ggml_set_name(tmp, "result_w3");
|
||||
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].w1,
|
||||
cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_w1");
|
||||
|
||||
// SILU activation
|
||||
cur = ggml_silu(ctx0, cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "silu");
|
||||
|
||||
cur = ggml_mul(ctx0, cur, tmp);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "silu_x_result_w3");
|
||||
|
||||
cur = ggml_mul_mat(ctx0,
|
||||
model.layers[il].w2,
|
||||
cur);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "result_w2");
|
||||
}
|
||||
|
||||
cur = ggml_add(ctx0, cur, inpFF);
|
||||
offload_func(cur);
|
||||
ggml_set_name(cur, "inpFF_+_result_w2");
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
// norm
|
||||
{
|
||||
cur = ggml_norm(ctx0, cur, norm_eps);
|
||||
offload_func_nr(cur);
|
||||
ggml_set_name(cur, "norm_2");
|
||||
|
||||
// cur = cur*norm(broadcasted)
|
||||
cur = ggml_mul(ctx0, cur, model.output_norm);
|
||||
// offload_func_nr(cur); // TODO CPU + GPU mirrored backend
|
||||
// ggml_set_name(cur, "result_norm");
|
||||
|
||||
cur = ggml_add(ctx0, cur, model.output_norm_b);
|
||||
offload_func_nr(cur);
|
||||
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 * llama_build_graph(
|
||||
llama_context & lctx,
|
||||
|
|
@ -5513,7 +5225,7 @@ static struct ggml_cgraph * llama_build_graph(
|
|||
} break;
|
||||
case LLM_ARCH_STABLELM:
|
||||
{
|
||||
result = llm_build_stablelm(lctx, batch);
|
||||
result = llm.build_stablelm();
|
||||
} break;
|
||||
default:
|
||||
GGML_ASSERT(false);
|
||||
|
|
@ -5689,7 +5401,8 @@ static int llama_decode_internal(
|
|||
model.arch == LLM_ARCH_BAICHUAN ||
|
||||
model.arch == LLM_ARCH_FALCON ||
|
||||
model.arch == LLM_ARCH_REFACT ||
|
||||
model.arch == LLM_ARCH_MPT;
|
||||
model.arch == LLM_ARCH_MPT ||
|
||||
model.arch == LLM_ARCH_STABLELM;
|
||||
|
||||
const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
|
||||
if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue