diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp index b9f3d684a..0fbb01d5d 100644 --- a/examples/baby-llama/baby-llama.cpp +++ b/examples/baby-llama/baby-llama.cpp @@ -137,6 +137,22 @@ struct llama_hparams { } }; + +struct llama_hparams_lora { + uint32_t n_vocab = 32000; + uint32_t n_ctx = 512; // this is provided as user input? + uint32_t n_embd = 4096; + uint32_t n_mult = 4; + uint32_t n_head = 32; + uint32_t n_layer = 32; + uint32_t n_rot = 64; + uint32_t n_lora = 64; + + bool operator!=(const llama_hparams & other) const { + return memcmp(this, &other, sizeof(llama_hparams)); + } +}; + struct llama_layer { // normalization struct ggml_tensor * attention_norm; @@ -156,6 +172,29 @@ struct llama_layer { struct ggml_tensor * w3; }; +struct llama_layer_lora { + // normalization + struct ggml_tensor * attention_norm; + + // attention + struct ggml_tensor * wqa; + struct ggml_tensor * wqb; + struct ggml_tensor * wka; + struct ggml_tensor * wkb; + struct ggml_tensor * wva; + struct ggml_tensor * wvb; + struct ggml_tensor * woa; + struct ggml_tensor * wob; + + // normalization + struct ggml_tensor * ffn_norm; + + // ff + struct ggml_tensor * w1; + struct ggml_tensor * w2; + struct ggml_tensor * w3; +}; + struct llama_kv_cache { struct ggml_context * ctx = NULL; @@ -181,6 +220,20 @@ struct llama_model { std::vector layers; }; +struct llama_model_lora { + struct ggml_context * ctx = NULL; + + llama_hparams_lora hparams; + + struct ggml_tensor * tok_embeddings; + + struct ggml_tensor * norm; + struct ggml_tensor * outputa; + struct ggml_tensor * outputb; + + std::vector layers; +}; + void init_model(struct llama_model * model) { const auto & hparams = model->hparams; @@ -217,6 +270,49 @@ void init_model(struct llama_model * model) { } } + +void init_model_lora(struct llama_model_lora * model) { + const auto & hparams = model->hparams; + + const uint32_t n_embd = hparams.n_embd; + const uint32_t n_layer = hparams.n_layer; + const uint32_t n_vocab = hparams.n_vocab; + const uint32_t n_lora = hparams.n_lora; + + uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult; + + struct ggml_context * ctx = model->ctx; + + model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); // ("tok_embeddings.weight", {n_embd, n_vocab}); + model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // ("norm.weight", {n_embd}); + model->outputa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_vocab); // ("output.weight", {n_embd, n_vocab}); + model->outputb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // ("output.weight", {n_embd, n_vocab}); + + model->layers.resize(n_layer); + for (uint32_t i = 0; i < n_layer; ++i) { + auto & layer = model->layers[i]; + + // std::string layers_i = "layers." + std::to_string(i); + + layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".attention_norm.weight", {n_embd}); + + layer.wqa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wq.weight", {n_embd, n_embd}); + layer.wqb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wq.weight", {n_embd, n_embd}); + layer.wka = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wk.weight", {n_embd, n_embd}); + layer.wkb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wk.weight", {n_embd, n_embd}); + layer.wva = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wv.weight", {n_embd, n_embd}); + layer.wvb = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wv.weight", {n_embd, n_embd}); + layer.woa = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_lora, n_embd); // (layers_i + ".attention.wo.weight", {n_embd, n_embd}); + layer.wob = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_lora); // (layers_i + ".attention.wo.weight", {n_embd, n_embd}); + + layer.ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); // (layers_i + ".ffn_norm.weight", {n_embd}); + + layer.w1 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w1.weight", {n_embd, n_ff}); + layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd); // (layers_i + ".feed_forward.w2.weight", { n_ff, n_embd}); + layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); // (layers_i + ".feed_forward.w3.weight", {n_embd, n_ff}); + } +} + void set_param_model(struct llama_model * model) { const auto& hparams = model->hparams; const uint32_t n_layer = hparams.n_layer; @@ -241,6 +337,35 @@ void set_param_model(struct llama_model * model) { } } +void set_param_model_lora(struct llama_model_lora * model) { + const auto& hparams = model->hparams; + const uint32_t n_layer = hparams.n_layer; + struct ggml_context* ctx = model->ctx; + + ggml_set_param(ctx, model->tok_embeddings); + ggml_set_param(ctx, model->norm); + ggml_set_param(ctx, model->outputa); + ggml_set_param(ctx, model->outputb); + + for (uint32_t i = 0; i < n_layer; ++i) { + auto & layer = model->layers[i]; + + ggml_set_param(ctx, layer.attention_norm); + ggml_set_param(ctx, layer.wqa); + ggml_set_param(ctx, layer.wqb); + ggml_set_param(ctx, layer.wka); + ggml_set_param(ctx, layer.wkb); + ggml_set_param(ctx, layer.wva); + ggml_set_param(ctx, layer.wvb); + ggml_set_param(ctx, layer.woa); + ggml_set_param(ctx, layer.wob); + ggml_set_param(ctx, layer.ffn_norm); + ggml_set_param(ctx, layer.w1); + ggml_set_param(ctx, layer.w2); + ggml_set_param(ctx, layer.w3); + } +} + void randomize_model(struct llama_model * model, int seed, float mean, float std, float min, float max) { const auto & hparams = model->hparams; @@ -273,6 +398,44 @@ void randomize_model(struct llama_model * model, int seed, float mean, float std } } + +void randomize_model_lora(struct llama_model_lora * model, int seed, float mean, float std, float min, float max) { + const auto & hparams = model->hparams; + + const uint32_t n_embd = hparams.n_embd; + const uint32_t n_layer = hparams.n_layer; + const uint32_t n_vocab = hparams.n_vocab; + + uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult; + + struct random_normal_distribution rnd; + init_random_normal_distribution(&rnd, seed, mean, std, min, max); + randomize_tensor_normal(model->tok_embeddings, model->tok_embeddings->n_dims, model->tok_embeddings->ne, &rnd); + randomize_tensor_normal(model->norm, model->norm->n_dims, model->norm->ne, &rnd); + randomize_tensor_normal(model->outputa, model->outputa->n_dims, model->outputa->ne, &rnd); + randomize_tensor_normal(model->outputb, model->outputb->n_dims, model->outputb->ne, &rnd); + + for (uint32_t i = 0; i < n_layer; ++i) { + auto & layer = model->layers[i]; + randomize_tensor_normal(layer.attention_norm, layer.attention_norm->n_dims, layer.attention_norm->ne, &rnd); + + randomize_tensor_normal(layer.wqa, layer.wqa->n_dims, layer.wqa->ne, &rnd); + randomize_tensor_normal(layer.wqb, layer.wqb->n_dims, layer.wqb->ne, &rnd); + randomize_tensor_normal(layer.wka, layer.wka->n_dims, layer.wka->ne, &rnd); + randomize_tensor_normal(layer.wkb, layer.wkb->n_dims, layer.wkb->ne, &rnd); + randomize_tensor_normal(layer.wva, layer.wva->n_dims, layer.wva->ne, &rnd); + randomize_tensor_normal(layer.wvb, layer.wvb->n_dims, layer.wvb->ne, &rnd); + randomize_tensor_normal(layer.woa, layer.woa->n_dims, layer.woa->ne, &rnd); + randomize_tensor_normal(layer.wob, layer.wob->n_dims, layer.wob->ne, &rnd); + + randomize_tensor_normal(layer.ffn_norm, layer.ffn_norm->n_dims, layer.ffn_norm->ne, &rnd); + + randomize_tensor_normal(layer.w1, layer.w1->n_dims, layer.w1->ne, &rnd); + randomize_tensor_normal(layer.w2, layer.w2->n_dims, layer.w2->ne, &rnd); + randomize_tensor_normal(layer.w3, layer.w3->n_dims, layer.w3->ne, &rnd); + } +} + bool init_kv_cache(struct llama_kv_cache* cache, struct llama_model * model) { const auto & hparams = model->hparams; const int n_ctx = hparams.n_ctx; @@ -308,6 +471,41 @@ bool init_kv_cache(struct llama_kv_cache* cache, struct llama_model * model) { return true; } +bool init_kv_cache_lora(struct llama_kv_cache* cache, struct llama_model_lora * model) { + const auto & hparams = model->hparams; + const int n_ctx = hparams.n_ctx; + const int n_embd = hparams.n_embd; + const int n_layer = hparams.n_layer; + + const int64_t n_mem = n_layer*n_ctx; + const int64_t n_elements = n_embd*n_mem; + + // cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB); + + // struct ggml_init_params params; + // params.mem_size = cache.buf.size; + // params.mem_buffer = cache.buf.addr; + // params.no_alloc = false; + if (!cache->ctx) { + struct ggml_init_params params; + params.mem_size = 2u*n_elements*ggml_type_size(GGML_TYPE_F32) + 2u*1024*1024; + params.mem_buffer = NULL; + params.no_alloc = false; + + cache->ctx = ggml_init(params); + + if (!cache->ctx) { + fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__); + return false; + } + } + + cache->k = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements); + cache->v = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements); + + return true; +} + struct ggml_tensor * forward( struct llama_model * model, struct llama_kv_cache * cache, @@ -452,6 +650,7 @@ struct ggml_tensor * forward( // ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); // projection (no bias) + // cur shape [n_embd,N,1,1] cur = ggml_mul_mat(ctx0, model->layers[il].wo, cur); @@ -459,50 +658,62 @@ struct ggml_tensor * forward( // lctx.use_buf(ctx0, 1); + // inpFF shape [n_embd,N,1,1] struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA); // feed-forward network { // norm { + // cur shape [n_embd,N,1,1] cur = ggml_rms_norm(ctx0, inpFF); // cur = ffn_norm*cur + // cur shape [n_embd,N,1,1] cur = ggml_mul(ctx0, ggml_repeat(ctx0, model->layers[il].ffn_norm, cur), cur); } + // tmp shape [n_ff,N,1,1] struct ggml_tensor * tmp = ggml_mul_mat(ctx0, model->layers[il].w3, cur); + // cur shape [n_ff,N,1,1] cur = ggml_mul_mat(ctx0, model->layers[il].w1, cur); // SILU activation + // cur shape [n_ff,N,1,1] cur = ggml_silu(ctx0, cur); + // cur shape [n_ff,N,1,1] cur = ggml_mul(ctx0, cur, tmp); + // cur shape [n_embd,N,1,1] cur = ggml_mul_mat(ctx0, model->layers[il].w2, cur); } + // cur shape [n_embd,N,1,1] cur = ggml_add(ctx0, cur, inpFF); // input for next layer + // inpL shape [n_embd,N,1,1] inpL = cur; } // norm { + // inpL shape [n_embd,N,1,1] inpL = ggml_rms_norm(ctx0, inpL); // inpL = norm*inpL + // inpL shape [n_embd,N,1,1] inpL = ggml_mul(ctx0, ggml_repeat(ctx0, model->norm, inpL), inpL); @@ -511,6 +722,7 @@ struct ggml_tensor * forward( } // lm_head + // inpL shape [n_vocab,N,1,1] inpL = ggml_mul_mat(ctx0, model->output, inpL); // run the computation @@ -519,6 +731,261 @@ struct ggml_tensor * forward( return inpL; } + +struct ggml_tensor * forward_lora( + struct llama_model_lora * model, + struct llama_kv_cache * cache, + struct ggml_context * ctx0, + struct ggml_cgraph * gf, + struct ggml_tensor * tokens_input, + const int n_tokens, + const int n_past) { + + const int N = n_tokens; + + struct llama_kv_cache& kv_self = *cache; + const auto & hparams = model->hparams; + const int n_ctx = hparams.n_ctx; + const int n_embd = hparams.n_embd; + const int n_layer = hparams.n_layer; + const int n_head = hparams.n_head; + const int n_rot = hparams.n_rot; + const int n_lora = hparams.n_lora; + + struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); + memcpy(tokens->data, tokens_input->data, N*ggml_element_size(tokens)); + + struct ggml_tensor * kc = kv_self.k; + struct ggml_tensor * vc = kv_self.v; + + // inpL shape [n_embd,N,1,1] + struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens); + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + struct ggml_tensor * cur; + + // norm + { + // cur shape [n_embd,N,1,1] + cur = ggml_rms_norm(ctx0, inpL); + + // cur = attention_norm*cur + cur = ggml_mul(ctx0, + ggml_repeat(ctx0, model->layers[il].attention_norm, cur), + cur); + } + + // self-attention + { + // compute Q and K and RoPE them + // wq shape [n_embd, n_embd, 1, 1] + // wk shape [n_embd, n_embd, 1, 1] + // Qcur shape [n_embd/n_head, n_head, N, 1] + // Kcur shape [n_embd/n_head, n_head, N, 1] + struct ggml_tensor * Qcur = ggml_rope(ctx0, + ggml_reshape_3d(ctx0, + ggml_mul_mat(ctx0, + model->layers[il].wqa, + ggml_mul_mat(ctx0, + model->layers[il].wqb, + cur)), + n_embd/n_head, n_head, N), + n_past, n_rot, 0); + struct ggml_tensor * Kcur = ggml_rope(ctx0, + ggml_reshape_3d(ctx0, + ggml_mul_mat(ctx0, + model->layers[il].wka, + ggml_mul_mat(ctx0, + model->layers[il].wkb, + cur)), + n_embd/n_head, n_head, N), + n_past, n_rot, 0); + + // store key and value to memory + { + // compute the transposed [N, n_embd] V matrix + // wv shape [n_embd, n_embd, 1, 1] + // Vcur shape [n_embd, N, 1, 1] + struct ggml_tensor * Vcur = ggml_cont(ctx0, + ggml_transpose(ctx0, + ggml_reshape_2d(ctx0, + ggml_mul_mat(ctx0, + model->layers[il].wva, + ggml_mul_mat(ctx0, + model->layers[il].wvb, + cur)), + n_embd, N))); + + // kv_self.k shape [n_embd * n_ctx * n_layer, 1] + // kv_self.v shape [n_embd * n_ctx * n_layer, 1] + // k shape [n_embd * N, 1] == kv_self.k[:,n_past:n_past+N,il,0] + // v shape [N, n_embd, 1, 1] == kv_self.v[:,n_past:n_past+N,il,0] + + /* { + struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past)); + struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd, + ( n_ctx)*ggml_element_size(kv_self.v), + (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.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)); + } //*/ + + kc = ggml_set_1d(ctx0, kc, ggml_reshape_1d(ctx0, Kcur, n_embd*N), (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past)); + vc = ggml_set_2d(ctx0, vc, Vcur, ( n_ctx)*ggml_element_size(kv_self.v), + (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v)); + } + + // Qcur shape [n_embd/n_head, n_head, N, 1] + // Q shape [n_embd/n_head, N, n_head, 1] + struct ggml_tensor * Q = + ggml_permute(ctx0, + Qcur, + 0, 2, 1, 3); + + // kv_self.k shape [n_embd * n_ctx * n_layer, 1] + // K shape [n_embd/n_head, n_past + N, n_head, 1] + struct ggml_tensor * K = + ggml_permute(ctx0, + ggml_reshape_3d(ctx0, + ggml_view_1d(ctx0, kc, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kc)*n_embd), + n_embd/n_head, n_head, n_past + N), + 0, 2, 1, 3); + + // K * Q + // KQ shape [n_past + N, N, n_head, 1] + struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); + + // KQ_scaled = KQ / sqrt(n_embd/n_head) + // KQ_scaled shape [n_past + N, N, n_head, 1] + struct ggml_tensor * KQ_scaled = + ggml_scale(ctx0, + KQ, + ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head))); + + // KQ_masked = mask_past(KQ_scaled) + // KQ_masked shape [n_past + N, N, n_head, 1] + struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past); + + // KQ = soft_max(KQ_masked) + // KQ_soft_max shape [n_past + N, N, n_head, 1] + struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked); + + // split cached V into n_head heads + //// V shape [n_past + N, n_embd/n_head, n_head, 1] + // V shape [n_past + N, n_embd/n_head, n_head, 1] == kv_self.v[:,:(n_past+N),il,1] + struct ggml_tensor * V = + ggml_view_3d(ctx0, vc, + n_past + N, n_embd/n_head, n_head, + n_ctx*ggml_element_size(vc), + n_ctx*ggml_element_size(vc)*n_embd/n_head, + il*n_ctx*ggml_element_size(vc)*n_embd); + + // KQV shape [n_embd/n_head, N, n_head, 1] + struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); + + // KQV_merged = KQV.permute(0, 2, 1, 3) + // KQV_merged shape [n_embd/n_head, n_head, N, 1] + struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); + // KQV_merged shape + + // cur = KQV_merged.contiguous().view(n_embd, N) + // cur shape [n_embd,N,1,1] + cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N); + // cur = ggml_cpy(ctx0, + // KQV_merged, + // ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); + + // projection (no bias) + // cur shape [n_embd,N,1,1] + cur = ggml_mul_mat(ctx0, + model->layers[il].woa, + ggml_mul_mat(ctx0, + model->layers[il].wob, + cur)); + } + + // inpFF shape [n_embd,N,1,1] + struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA); + + // feed-forward network + { + // norm + { + // cur shape [n_embd,N,1,1] + cur = ggml_rms_norm(ctx0, inpFF); + + // cur = ffn_norm*cur + // cur shape [n_embd,N,1,1] + cur = ggml_mul(ctx0, + ggml_repeat(ctx0, model->layers[il].ffn_norm, cur), + cur); + } + + // tmp shape [n_ff,N,1,1] + struct ggml_tensor * tmp = ggml_mul_mat(ctx0, + model->layers[il].w3, + cur); + + // cur shape [n_ff,N,1,1] + cur = ggml_mul_mat(ctx0, + model->layers[il].w1, + cur); + + // SILU activation + // cur shape [n_ff,N,1,1] + cur = ggml_silu(ctx0, cur); + + // cur shape [n_ff,N,1,1] + cur = ggml_mul(ctx0, cur, tmp); + + // cur shape [n_embd,N,1,1] + cur = ggml_mul_mat(ctx0, + model->layers[il].w2, + cur); + } + + // cur shape [n_embd,N,1,1] + cur = ggml_add(ctx0, cur, inpFF); + + // input for next layer + // inpL shape [n_embd,N,1,1] + inpL = cur; + } + + // norm + { + + // inpL shape [n_embd,N,1,1] + inpL = ggml_rms_norm(ctx0, inpL); + + // inpL = norm*inpL + // inpL shape [n_embd,N,1,1] + inpL = ggml_mul(ctx0, + ggml_repeat(ctx0, model->norm, inpL), + inpL); + + //embeddings = inpL; + } + + + // lm_head + // inpL shape [n_vocab,N,1,1] + inpL = ggml_mul_mat(ctx0, + model->outputa, + ggml_mul_mat(ctx0, + model->outputb, + inpL)); + + // ggml_set_scratch(ctx0, { 0, 0, nullptr, }); + // run the computation + ggml_build_forward_expand(gf, inpL); + + return inpL; +} + void sample_softmax(struct ggml_tensor * logits, struct ggml_tensor * probs, struct ggml_tensor * best_samples) { assert(logits->n_dims == 2); assert(probs->n_dims == 2); @@ -665,12 +1132,46 @@ int main(int argc, char ** argv) { randomize_model(&model, 1337, 0.0f, 1.0f, -1.0f, +1.0f); +/* + struct llama_model_lora model_lora; + // model.hparams.n_vocab = 6; + // model.hparams.n_ctx = 64; + // model.hparams.n_embd = 128; + // model.hparams.n_mult = 2; + // model.hparams.n_head = 8; + // model.hparams.n_layer = 6; + // model.hparams.n_rot = model.hparams.n_embd / model.hparams.n_head; + + model_lora.hparams.n_vocab = 16; + model_lora.hparams.n_ctx = 32; + model_lora.hparams.n_embd = 256; + model_lora.hparams.n_mult = 2; + model_lora.hparams.n_head = 16; + model_lora.hparams.n_layer = 1; + model_lora.hparams.n_lora = 64; + model_lora.hparams.n_rot = MIN(16, model_lora.hparams.n_embd / model_lora.hparams.n_head); + // model.hparams.n_rot = (model.hparams.n_embd / model.hparams.n_head) / 2; + + // model.hparams.n_embd = 32; + // model.hparams.n_mult = 2; + // model.hparams.n_head = 4; + // model.hparams.n_layer = 8; + // model.hparams.n_rot = 8; + + model_lora.ctx = ggml_init(lcparams); + printf("init model_lora\n"); + init_model_lora(&model_lora); + set_param_model_lora(&model_lora); + + randomize_model_lora(&model_lora, 1337, 0.0f, 1.0f, -1.0f, +1.0f); +*/ + // key + value cache for the self attention struct llama_kv_cache kv_self; printf("init_kv_cache\n"); kv_self.ctx = model.ctx; init_kv_cache(&kv_self, &model); - + //init_kv_cache_lora(&kv_self, &model_lora); size_t compute_size = 1024ll*1024ll*1024ll; uint8_t * compute_addr = new uint8_t[compute_size]; @@ -842,6 +1343,7 @@ int main(int argc, char ** argv) { printf("done\n"); // ggml_free(kv_self.ctx); + // ggml_free(model_lora.ctx); ggml_free(model.ctx); return 0; }