# include "ggml-cuda.h"
#elif defined(GGML_USE_CLBLAST)
# include "ggml-opencl.h"
+#elif defined(GGML_USE_VULKAN)
+# include "ggml-vulkan.h"
+#elif defined(GGML_USE_SYCL)
+# include "ggml-sycl.h"
#endif
#ifdef GGML_USE_METAL
#include <algorithm>
#include <array>
#include <cassert>
+#include <cfloat>
#include <cinttypes>
#include <climits>
#include <cmath>
LLM_ARCH_PHI2,
LLM_ARCH_PLAMO,
LLM_ARCH_CODESHELL,
+ LLM_ARCH_ORION,
LLM_ARCH_UNKNOWN,
};
{ LLM_ARCH_PHI2, "phi2" },
{ LLM_ARCH_PLAMO, "plamo" },
{ LLM_ARCH_CODESHELL, "codeshell" },
+ { LLM_ARCH_ORION, "orion" },
};
enum llm_kv {
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
+ {
+ LLM_ARCH_ORION,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
+ { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
+ { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ },
+ },
{
LLM_ARCH_UNKNOWN,
if (host_buffer) {
buft = ggml_backend_cuda_host_buffer_type();
}
+#elif defined(GGML_USE_SYCL)
+ buft = ggml_backend_sycl_host_buffer_type();
#elif defined(GGML_USE_CPU_HBM)
buft = ggml_backend_cpu_hbm_buffer_type();
+#elif defined(GGML_USE_VULKAN)
+ if (host_buffer) {
+ buft = ggml_backend_vk_host_buffer_type();
+ }
#endif
if (buft == nullptr) {
buft = ggml_backend_metal_buffer_type();
#elif defined(GGML_USE_CUBLAS)
buft = ggml_backend_cuda_buffer_type(gpu);
+#elif defined(GGML_USE_VULKAN)
+ buft = ggml_backend_vk_buffer_type();
+#elif defined(GGML_USE_SYCL)
+ buft = ggml_backend_sycl_buffer_type(gpu);
#elif defined(GGML_USE_CLBLAST)
buft = ggml_backend_opencl_buffer_type();
#endif
MODEL_7B,
MODEL_8B,
MODEL_13B,
+ MODEL_14B,
MODEL_15B,
MODEL_30B,
MODEL_34B,
case MODEL_7B: return "7B";
case MODEL_8B: return "8B";
case MODEL_13B: return "13B";
+ case MODEL_14B: return "14B";
case MODEL_15B: return "15B";
case MODEL_30B: return "30B";
case MODEL_34B: return "34B";
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
+ case LLM_ARCH_ORION:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ switch (hparams.n_layer) {
+ case 40: model.type = e_model::MODEL_14B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
default: (void)0;
}
layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
}
} break;
+ case LLM_ARCH_ORION:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+ model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab});
+ }
+ for (int i = 0; i < n_layer; ++i) {
+ ggml_context * ctx_layer = ctx_for_layer(i);
+ ggml_context * ctx_split = ctx_for_layer_split(i);
+
+ auto & layer = model.layers[i];
+
+ layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+ layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd});
+
+ layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
+ layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
+ layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+ layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd});
+
+ layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
+ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd});
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ }
+ } break;
+
+
default:
throw std::runtime_error("unknown architecture");
}
ctx0 = nullptr;
}
}
+ struct ggml_cgraph * build_orion() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+ GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb);
+ cb(inpL, "inp_embd", -1);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0);
+ cb(inp_pos, "inp_pos", -1);
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0);
+ cb(KQ_mask, "KQ_mask", -1);
+
+ // shift the entire K-cache if needed
+ if (do_rope_shift) {
+ llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb);
+ }
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * inpSA = inpL;
+
+ // norm
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, model.layers[il].attn_norm_b,
+ LLM_NORM, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+ // if (model.layers[il].bq) {
+ // Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+ // cb(Qcur, "Qcur", il);
+ // }
+
+ struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+ // if (model.layers[il].bk) {
+ // Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+ // cb(Kcur, "Kcur", il);
+ // }
+
+ struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+ // if (model.layers[il].bv) {
+ // Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+ // cb(Vcur, "Vcur", il);
+ // }
+
+ Qcur = ggml_rope_custom(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos,
+ hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_custom(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos,
+ hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
+ model.layers[il].wo, NULL,
+ Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ cb(cur, "kqv_out", il);
+ }
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
+ LLM_NORM, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, cur,
+ model.layers[il].ffn_up, NULL,
+ model.layers[il].ffn_gate, NULL,
+ model.layers[il].ffn_down, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, model.output_norm_b,
+ LLM_NORM, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = ggml_mul_mat(ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+
struct ggml_cgraph * build_llama() {
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
{
result = llm.build_codeshell();
} break;
+ case LLM_ARCH_ORION:
+ {
+ result = llm.build_orion();
+ } break;
default:
GGML_ASSERT(false);
}
}
const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 1;
- if (ggml_cpu_has_cublas() && fully_offloaded) {
+ if ((ggml_cpu_has_cublas() || ggml_cpu_has_vulkan()) && fully_offloaded) {
n_threads = 1;
}
}
void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int32_t k, size_t min_keep) {
+ // TODO: move bucket sort to separate function so that top_p/tail_free/typical/softmax first is equally fast
+ // if (k >= (int32_t)candidates->size) {
+ // return;
+ // }
+
const int64_t t_start_sample_us = ggml_time_us();
k = std::max(k, (int) min_keep);
return;
}
- llama_sample_softmax(ctx, candidates);
-
const int64_t t_start_sample_us = ggml_time_us();
- float scale = candidates->data[0].p; // scale by max prob
- size_t i = 1; // first token always matches
+ bool min_p_applied = false;
+
+ // if the candidates aren't sorted, try the unsorted implementation first
+ if (!candidates->sorted) {
+ std::vector<llama_token_data> filtered_tokens;
- for (; i < candidates->size; ++i) {
- if (candidates->data[i].p < p * scale && i >= min_keep) {
- break; // prob too small
+ float max_logit = -FLT_MAX;
+ for (size_t i = 0; i < candidates->size; ++i) {
+ max_logit = std::max(max_logit, candidates->data[i].logit);
+ }
+ const float min_logit = max_logit + logf(p); // min logit for p_i >= p * p_max
+
+ for (size_t i = 0; i < candidates->size; ++i) {
+ if (candidates->data[i].logit >= min_logit) {
+ filtered_tokens.push_back(candidates->data[i]);
+ }
+ }
+
+ // if we have enough values the operation was a success
+ if (filtered_tokens.size() >= min_keep) {
+ memcpy(candidates->data, filtered_tokens.data(), filtered_tokens.size()*sizeof(llama_token_data));
+ candidates->size = filtered_tokens.size();
+ min_p_applied = true;
}
}
- // Resize the output vector to keep only the matching tokens
- candidates->size = i;
+ // if the candidates are sorted or the unsorted implementation failed, use this implementation
+ if (!min_p_applied) {
+ // Sort the logits in descending order
+ if (!candidates->sorted) {
+ std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) {
+ return a.logit > b.logit;
+ });
+ candidates->sorted = true;
+ }
+
+ const float min_logit = candidates->data[0].logit + logf(p); // min logit for p_i >= p * p_max
+ size_t i = 1; // first token always matches
+
+ for (; i < candidates->size; ++i) {
+ if (candidates->data[i].logit < min_logit && i >= min_keep) {
+ break; // prob too small
+ }
+ }
+
+ // Resize the output vector to keep only the matching tokens
+ candidates->size = i;
+ }
if (ctx) {
ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
}
}
}
+#elif defined(GGML_USE_VULKAN)
+ if (model->n_gpu_layers > 0) {
+ ggml_backend_t backend = ggml_backend_vk_init();
+ if (backend == nullptr) {
+ LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__);
+ llama_free(ctx);
+ return nullptr;
+ }
+ ctx->backends.push_back(backend);
+ }
+#elif defined(GGML_USE_SYCL)
+ if (model->n_gpu_layers > 0) {
+ ggml_backend_t backend = ggml_backend_sycl_init(model->main_gpu);
+ if (backend == nullptr) {
+ LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d backend\n", __func__, model->main_gpu);
+ llama_free(ctx);
+ return nullptr;
+ }
+ ctx->backends.push_back(backend);
+ }
#endif
ctx->backend_cpu = ggml_backend_cpu_init();
if (ctx->backend_cpu == nullptr) {
overview / pkg / ggml / sources / whisper.cpp / commitdiff