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authorKawrakow <48489457+ikawrakow@users.noreply.github.com>2024-07-27 07:55:01 +0200
committerGitHub <noreply@github.com>2024-07-27 07:55:01 +0200
commit154e0d75fccf1784fe9ff6fd76a630b66563da3d (patch)
tree81ce6dbb5b1900c1aa78a879f0593c694cab9d27 /src/llama.cpp
parent0684c3e9c70d49323b4fc517128cbe222cab7f96 (diff)
Merge mainline llama.cpp (#3)
* Merging mainline - WIP * Merging mainline - WIP AVX2 and CUDA appear to work. CUDA performance seems slightly (~1-2%) lower as it is so often the case with llama.cpp/ggml after some "improvements" have been made. * Merging mainline - fix Metal * Remove check --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Diffstat (limited to 'src/llama.cpp')
-rw-r--r--src/llama.cpp19336
1 files changed, 19336 insertions, 0 deletions
diff --git a/src/llama.cpp b/src/llama.cpp
new file mode 100644
index 00000000..eecfccbd
--- /dev/null
+++ b/src/llama.cpp
@@ -0,0 +1,19336 @@
+#include "llama-impl.h"
+#include "llama-vocab.h"
+#include "llama-grammar.h"
+#include "llama-sampling.h"
+
+#include "unicode.h"
+
+#include "ggml.h"
+#include "ggml-alloc.h"
+#include "ggml-backend.h"
+
+#ifdef GGML_USE_RPC
+# include "ggml-rpc.h"
+#endif
+
+#ifdef GGML_USE_CUDA
+# include "ggml-cuda.h"
+#elif defined(GGML_USE_VULKAN)
+# include "ggml-vulkan.h"
+#elif defined(GGML_USE_SYCL)
+# include "ggml-sycl.h"
+#elif defined(GGML_USE_KOMPUTE)
+# include "ggml-kompute.h"
+#elif defined(GGML_USE_CANN)
+# include "ggml-cann.h"
+#endif
+
+#ifdef GGML_USE_BLAS
+# include "ggml-blas.h"
+#endif
+
+#ifdef GGML_USE_METAL
+# include "ggml-metal.h"
+#endif
+
+// TODO: replace with ggml API call
+#define QK_K 256
+#define QK_IQ1BN 64
+
+#ifdef __has_include
+ #if __has_include(<unistd.h>)
+ #include <unistd.h>
+ #if defined(_POSIX_MAPPED_FILES)
+ #include <sys/mman.h>
+ #include <fcntl.h>
+ #endif
+ #if defined(_POSIX_MEMLOCK_RANGE)
+ #include <sys/resource.h>
+ #endif
+ #endif
+#endif
+
+#if defined(_WIN32)
+ #define WIN32_LEAN_AND_MEAN
+ #ifndef NOMINMAX
+ #define NOMINMAX
+ #endif
+ #include <windows.h>
+ #ifndef PATH_MAX
+ #define PATH_MAX MAX_PATH
+ #endif
+ #include <io.h>
+#endif
+
+#if __cplusplus >= 202000L
+ #define LU8(x) (const char*)(u8##x)
+#else
+ #define LU8(x) u8##x
+#endif
+
+#include <algorithm>
+#include <array>
+#include <cassert>
+#include <cctype>
+#include <cfloat>
+#include <cinttypes>
+#include <climits>
+#include <cmath>
+#include <cstdarg>
+#include <cstddef>
+#include <cstdint>
+#include <cstdio>
+#include <cstring>
+#include <ctime>
+#include <fstream>
+#include <functional>
+#include <future>
+#include <initializer_list>
+#include <locale>
+#include <map>
+#include <memory>
+#include <mutex>
+#include <numeric>
+#include <set>
+#include <sstream>
+#include <thread>
+#include <type_traits>
+#include <unordered_map>
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+// bump if necessary
+#define LLAMA_MAX_NODES 8192
+#define LLAMA_MAX_LAYERS 512
+#define LLAMA_MAX_EXPERTS 160 // DeepSeekV2
+
+//
+// helpers
+//
+
+// trim whitespace from the beginning and end of a string
+static std::string trim(const std::string & str) {
+ size_t start = 0;
+ size_t end = str.size();
+ while (start < end && isspace(str[start])) {
+ start += 1;
+ }
+ while (end > start && isspace(str[end - 1])) {
+ end -= 1;
+ }
+ return str.substr(start, end - start);
+}
+
+static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
+ std::string result;
+ for (size_t pos = 0; ; pos += search.length()) {
+ auto new_pos = s.find(search, pos);
+ if (new_pos == std::string::npos) {
+ result += s.substr(pos, s.size() - pos);
+ break;
+ }
+ result += s.substr(pos, new_pos - pos) + replace;
+ pos = new_pos;
+ }
+ s = std::move(result);
+}
+
+static bool is_float_close(float a, float b, float abs_tol) {
+ // Check for non-negative tolerance
+ if (abs_tol < 0.0) {
+ throw std::invalid_argument("Tolerance must be non-negative");
+ }
+
+ // Exact equality check
+ if (a == b) {
+ return true;
+ }
+
+ // Check for infinities
+ if (std::isinf(a) || std::isinf(b)) {
+ return false;
+ }
+
+ // Regular comparison using the provided absolute tolerance
+ return std::fabs(b - a) <= abs_tol;
+}
+
+static void zeros(std::ofstream & file, size_t n) {
+ char zero = 0;
+ for (size_t i = 0; i < n; ++i) {
+ file.write(&zero, 1);
+ }
+}
+
+LLAMA_ATTRIBUTE_FORMAT(1, 2)
+static std::string format(const char * fmt, ...) {
+ va_list ap;
+ va_list ap2;
+ va_start(ap, fmt);
+ va_copy(ap2, ap);
+ int size = vsnprintf(NULL, 0, fmt, ap);
+ GGML_ASSERT(size >= 0 && size < INT_MAX); // NOLINT
+ std::vector<char> buf(size + 1);
+ int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
+ GGML_ASSERT(size2 == size);
+ va_end(ap2);
+ va_end(ap);
+ return std::string(buf.data(), size);
+}
+
+//
+// gguf constants (sync with gguf.py)
+//
+
+enum llm_arch {
+ LLM_ARCH_LLAMA,
+ LLM_ARCH_FALCON,
+ LLM_ARCH_BAICHUAN,
+ LLM_ARCH_GROK,
+ LLM_ARCH_GPT2,
+ LLM_ARCH_GPTJ,
+ LLM_ARCH_GPTNEOX,
+ LLM_ARCH_MPT,
+ LLM_ARCH_STARCODER,
+ LLM_ARCH_REFACT,
+ LLM_ARCH_BERT,
+ LLM_ARCH_NOMIC_BERT,
+ LLM_ARCH_JINA_BERT_V2,
+ LLM_ARCH_BLOOM,
+ LLM_ARCH_STABLELM,
+ LLM_ARCH_QWEN,
+ LLM_ARCH_QWEN2,
+ LLM_ARCH_QWEN2MOE,
+ LLM_ARCH_PHI2,
+ LLM_ARCH_PHI3,
+ LLM_ARCH_PLAMO,
+ LLM_ARCH_CODESHELL,
+ LLM_ARCH_ORION,
+ LLM_ARCH_INTERNLM2,
+ LLM_ARCH_MINICPM,
+ LLM_ARCH_GEMMA,
+ LLM_ARCH_GEMMA2,
+ LLM_ARCH_STARCODER2,
+ LLM_ARCH_MAMBA,
+ LLM_ARCH_XVERSE,
+ LLM_ARCH_COMMAND_R,
+ LLM_ARCH_DBRX,
+ LLM_ARCH_OLMO,
+ LLM_ARCH_OPENELM,
+ LLM_ARCH_ARCTIC,
+ LLM_ARCH_DEEPSEEK2,
+ LLM_ARCH_CHATGLM,
+ LLM_ARCH_BITNET,
+ LLM_ARCH_T5,
+ LLM_ARCH_JAIS,
+ LLM_ARCH_UNKNOWN,
+};
+
+static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
+ { LLM_ARCH_LLAMA, "llama" },
+ { LLM_ARCH_FALCON, "falcon" },
+ { LLM_ARCH_GROK, "grok" },
+ { LLM_ARCH_GPT2, "gpt2" },
+ { LLM_ARCH_GPTJ, "gptj" },
+ { LLM_ARCH_GPTNEOX, "gptneox" },
+ { LLM_ARCH_MPT, "mpt" },
+ { LLM_ARCH_BAICHUAN, "baichuan" },
+ { LLM_ARCH_STARCODER, "starcoder" },
+ { LLM_ARCH_REFACT, "refact" },
+ { LLM_ARCH_BERT, "bert" },
+ { LLM_ARCH_NOMIC_BERT, "nomic-bert" },
+ { LLM_ARCH_JINA_BERT_V2, "jina-bert-v2" },
+ { LLM_ARCH_BLOOM, "bloom" },
+ { LLM_ARCH_STABLELM, "stablelm" },
+ { LLM_ARCH_QWEN, "qwen" },
+ { LLM_ARCH_QWEN2, "qwen2" },
+ { LLM_ARCH_QWEN2MOE, "qwen2moe" },
+ { LLM_ARCH_PHI2, "phi2" },
+ { LLM_ARCH_PHI3, "phi3" },
+ { LLM_ARCH_PLAMO, "plamo" },
+ { LLM_ARCH_CODESHELL, "codeshell" },
+ { LLM_ARCH_ORION, "orion" },
+ { LLM_ARCH_INTERNLM2, "internlm2" },
+ { LLM_ARCH_MINICPM, "minicpm" },
+ { LLM_ARCH_GEMMA, "gemma" },
+ { LLM_ARCH_GEMMA2, "gemma2" },
+ { LLM_ARCH_STARCODER2, "starcoder2" },
+ { LLM_ARCH_MAMBA, "mamba" },
+ { LLM_ARCH_XVERSE, "xverse" },
+ { LLM_ARCH_COMMAND_R, "command-r" },
+ { LLM_ARCH_DBRX, "dbrx" },
+ { LLM_ARCH_OLMO, "olmo" },
+ { LLM_ARCH_OPENELM, "openelm" },
+ { LLM_ARCH_ARCTIC, "arctic" },
+ { LLM_ARCH_DEEPSEEK2, "deepseek2" },
+ { LLM_ARCH_CHATGLM, "chatglm" },
+ { LLM_ARCH_BITNET, "bitnet" },
+ { LLM_ARCH_T5, "t5" },
+ { LLM_ARCH_JAIS, "jais" },
+ { LLM_ARCH_UNKNOWN, "(unknown)" },
+};
+
+enum llm_kv {
+ LLM_KV_GENERAL_TYPE,
+ LLM_KV_GENERAL_ARCHITECTURE,
+ LLM_KV_GENERAL_QUANTIZATION_VERSION,
+ LLM_KV_GENERAL_ALIGNMENT,
+ LLM_KV_GENERAL_NAME,
+ LLM_KV_GENERAL_AUTHOR,
+ LLM_KV_GENERAL_VERSION,
+ LLM_KV_GENERAL_URL,
+ LLM_KV_GENERAL_DESCRIPTION,
+ LLM_KV_GENERAL_LICENSE,
+ LLM_KV_GENERAL_SOURCE_URL,
+ LLM_KV_GENERAL_SOURCE_HF_REPO,
+
+ LLM_KV_VOCAB_SIZE,
+ LLM_KV_CONTEXT_LENGTH,
+ LLM_KV_EMBEDDING_LENGTH,
+ LLM_KV_BLOCK_COUNT,
+ LLM_KV_LEADING_DENSE_BLOCK_COUNT,
+ LLM_KV_FEED_FORWARD_LENGTH,
+ LLM_KV_EXPERT_FEED_FORWARD_LENGTH,
+ LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH,
+ LLM_KV_USE_PARALLEL_RESIDUAL,
+ LLM_KV_TENSOR_DATA_LAYOUT,
+ LLM_KV_EXPERT_COUNT,
+ LLM_KV_EXPERT_USED_COUNT,
+ LLM_KV_EXPERT_SHARED_COUNT,
+ LLM_KV_EXPERT_WEIGHTS_SCALE,
+ LLM_KV_POOLING_TYPE,
+ LLM_KV_LOGIT_SCALE,
+ LLM_KV_DECODER_START_TOKEN_ID,
+ LLM_KV_ATTN_LOGIT_SOFTCAPPING,
+ LLM_KV_FINAL_LOGIT_SOFTCAPPING,
+
+ LLM_KV_ATTENTION_HEAD_COUNT,
+ LLM_KV_ATTENTION_HEAD_COUNT_KV,
+ LLM_KV_ATTENTION_MAX_ALIBI_BIAS,
+ LLM_KV_ATTENTION_CLAMP_KQV,
+ LLM_KV_ATTENTION_KEY_LENGTH,
+ LLM_KV_ATTENTION_VALUE_LENGTH,
+ LLM_KV_ATTENTION_LAYERNORM_EPS,
+ LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,
+ LLM_KV_ATTENTION_CAUSAL,
+ LLM_KV_ATTENTION_Q_LORA_RANK,
+ LLM_KV_ATTENTION_KV_LORA_RANK,
+ LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
+ LLM_KV_ATTENTION_SLIDING_WINDOW,
+
+ LLM_KV_ROPE_DIMENSION_COUNT,
+ LLM_KV_ROPE_FREQ_BASE,
+ LLM_KV_ROPE_SCALE_LINEAR,
+ LLM_KV_ROPE_SCALING_TYPE,
+ LLM_KV_ROPE_SCALING_FACTOR,
+ LLM_KV_ROPE_SCALING_ATTN_FACTOR,
+ LLM_KV_ROPE_SCALING_ORIG_CTX_LEN,
+ LLM_KV_ROPE_SCALING_FINETUNED,
+ LLM_KV_ROPE_SCALING_YARN_LOG_MUL,
+
+ LLM_KV_SPLIT_NO,
+ LLM_KV_SPLIT_COUNT,
+ LLM_KV_SPLIT_TENSORS_COUNT,
+
+ LLM_KV_SSM_INNER_SIZE,
+ LLM_KV_SSM_CONV_KERNEL,
+ LLM_KV_SSM_STATE_SIZE,
+ LLM_KV_SSM_TIME_STEP_RANK,
+
+ LLM_KV_TOKENIZER_MODEL,
+ LLM_KV_TOKENIZER_PRE,
+ LLM_KV_TOKENIZER_LIST,
+ LLM_KV_TOKENIZER_TOKEN_TYPE,
+ LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,
+ LLM_KV_TOKENIZER_SCORES,
+ LLM_KV_TOKENIZER_MERGES,
+ LLM_KV_TOKENIZER_BOS_ID,
+ LLM_KV_TOKENIZER_EOS_ID,
+ LLM_KV_TOKENIZER_UNK_ID,
+ LLM_KV_TOKENIZER_SEP_ID,
+ LLM_KV_TOKENIZER_PAD_ID,
+ LLM_KV_TOKENIZER_CLS_ID,
+ LLM_KV_TOKENIZER_MASK_ID,
+ LLM_KV_TOKENIZER_ADD_BOS,
+ LLM_KV_TOKENIZER_ADD_EOS,
+ LLM_KV_TOKENIZER_ADD_PREFIX,
+ LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,
+ LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP,
+ LLM_KV_TOKENIZER_HF_JSON,
+ LLM_KV_TOKENIZER_RWKV,
+ LLM_KV_TOKENIZER_PREFIX_ID,
+ LLM_KV_TOKENIZER_SUFFIX_ID,
+ LLM_KV_TOKENIZER_MIDDLE_ID,
+ LLM_KV_TOKENIZER_EOT_ID,
+
+ LLM_KV_ADAPTER_TYPE,
+ LLM_KV_ADAPTER_LORA_ALPHA,
+};
+
+static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
+ { LLM_KV_GENERAL_TYPE, "general.type" },
+ { LLM_KV_GENERAL_ARCHITECTURE, "general.architecture" },
+ { LLM_KV_GENERAL_QUANTIZATION_VERSION, "general.quantization_version" },
+ { LLM_KV_GENERAL_ALIGNMENT, "general.alignment" },
+ { LLM_KV_GENERAL_NAME, "general.name" },
+ { LLM_KV_GENERAL_AUTHOR, "general.author" },
+ { LLM_KV_GENERAL_VERSION, "general.version" },
+ { LLM_KV_GENERAL_URL, "general.url" },
+ { LLM_KV_GENERAL_DESCRIPTION, "general.description" },
+ { LLM_KV_GENERAL_LICENSE, "general.license" },
+ { LLM_KV_GENERAL_SOURCE_URL, "general.source.url" },
+ { LLM_KV_GENERAL_SOURCE_HF_REPO, "general.source.huggingface.repository" },
+
+ { LLM_KV_VOCAB_SIZE, "%s.vocab_size" },
+ { LLM_KV_CONTEXT_LENGTH, "%s.context_length" },
+ { LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" },
+ { LLM_KV_BLOCK_COUNT, "%s.block_count" },
+ { LLM_KV_LEADING_DENSE_BLOCK_COUNT, "%s.leading_dense_block_count" },
+ { LLM_KV_FEED_FORWARD_LENGTH, "%s.feed_forward_length" },
+ { LLM_KV_EXPERT_FEED_FORWARD_LENGTH, "%s.expert_feed_forward_length" },
+ { LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, "%s.expert_shared_feed_forward_length" },
+ { LLM_KV_USE_PARALLEL_RESIDUAL, "%s.use_parallel_residual" },
+ { LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" },
+ { LLM_KV_EXPERT_COUNT, "%s.expert_count" },
+ { LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" },
+ { LLM_KV_EXPERT_SHARED_COUNT, "%s.expert_shared_count" },
+ { LLM_KV_EXPERT_WEIGHTS_SCALE, "%s.expert_weights_scale" },
+ { LLM_KV_POOLING_TYPE , "%s.pooling_type" },
+ { LLM_KV_LOGIT_SCALE, "%s.logit_scale" },
+ { LLM_KV_DECODER_START_TOKEN_ID, "%s.decoder_start_token_id" },
+ { LLM_KV_ATTN_LOGIT_SOFTCAPPING, "%s.attn_logit_softcapping" },
+ { LLM_KV_FINAL_LOGIT_SOFTCAPPING, "%s.final_logit_softcapping" },
+
+ { LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" },
+ { LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" },
+ { LLM_KV_ATTENTION_MAX_ALIBI_BIAS, "%s.attention.max_alibi_bias" },
+ { LLM_KV_ATTENTION_CLAMP_KQV, "%s.attention.clamp_kqv" },
+ { LLM_KV_ATTENTION_KEY_LENGTH, "%s.attention.key_length" },
+ { LLM_KV_ATTENTION_VALUE_LENGTH, "%s.attention.value_length" },
+ { LLM_KV_ATTENTION_LAYERNORM_EPS, "%s.attention.layer_norm_epsilon" },
+ { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, "%s.attention.layer_norm_rms_epsilon" },
+ { LLM_KV_ATTENTION_CAUSAL, "%s.attention.causal" },
+ { LLM_KV_ATTENTION_Q_LORA_RANK, "%s.attention.q_lora_rank" },
+ { LLM_KV_ATTENTION_KV_LORA_RANK, "%s.attention.kv_lora_rank" },
+ { LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" },
+ { LLM_KV_ATTENTION_SLIDING_WINDOW, "%s.attention.sliding_window" },
+
+ { LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" },
+ { LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" },
+ { LLM_KV_ROPE_SCALE_LINEAR, "%s.rope.scale_linear" },
+ { LLM_KV_ROPE_SCALING_TYPE, "%s.rope.scaling.type" },
+ { LLM_KV_ROPE_SCALING_FACTOR, "%s.rope.scaling.factor" },
+ { LLM_KV_ROPE_SCALING_ATTN_FACTOR, "%s.rope.scaling.attn_factor" },
+ { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" },
+ { LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" },
+ { LLM_KV_ROPE_SCALING_YARN_LOG_MUL, "%s.rope.scaling.yarn_log_multiplier" },
+
+ { LLM_KV_SPLIT_NO, "split.no" },
+ { LLM_KV_SPLIT_COUNT, "split.count" },
+ { LLM_KV_SPLIT_TENSORS_COUNT, "split.tensors.count" },
+
+ { LLM_KV_SSM_CONV_KERNEL, "%s.ssm.conv_kernel" },
+ { LLM_KV_SSM_INNER_SIZE, "%s.ssm.inner_size" },
+ { LLM_KV_SSM_STATE_SIZE, "%s.ssm.state_size" },
+ { LLM_KV_SSM_TIME_STEP_RANK, "%s.ssm.time_step_rank" },
+
+ { LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" },
+ { LLM_KV_TOKENIZER_PRE, "tokenizer.ggml.pre" },
+ { LLM_KV_TOKENIZER_LIST, "tokenizer.ggml.tokens" },
+ { LLM_KV_TOKENIZER_TOKEN_TYPE, "tokenizer.ggml.token_type" },
+ { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, "tokenizer.ggml.token_type_count" },
+ { LLM_KV_TOKENIZER_SCORES, "tokenizer.ggml.scores" },
+ { LLM_KV_TOKENIZER_MERGES, "tokenizer.ggml.merges" },
+ { LLM_KV_TOKENIZER_BOS_ID, "tokenizer.ggml.bos_token_id" },
+ { LLM_KV_TOKENIZER_EOS_ID, "tokenizer.ggml.eos_token_id" },
+ { LLM_KV_TOKENIZER_UNK_ID, "tokenizer.ggml.unknown_token_id" },
+ { LLM_KV_TOKENIZER_SEP_ID, "tokenizer.ggml.seperator_token_id" },
+ { LLM_KV_TOKENIZER_PAD_ID, "tokenizer.ggml.padding_token_id" },
+ { LLM_KV_TOKENIZER_CLS_ID, "tokenizer.ggml.cls_token_id" },
+ { LLM_KV_TOKENIZER_MASK_ID, "tokenizer.ggml.mask_token_id" },
+ { LLM_KV_TOKENIZER_ADD_BOS, "tokenizer.ggml.add_bos_token" },
+ { LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" },
+ { LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" },
+ { LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, "tokenizer.ggml.remove_extra_whitespaces" },
+ { LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, "tokenizer.ggml.precompiled_charsmap" },
+ { LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" },
+ { LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" },
+ { LLM_KV_TOKENIZER_PREFIX_ID, "tokenizer.ggml.prefix_token_id" },
+ { LLM_KV_TOKENIZER_SUFFIX_ID, "tokenizer.ggml.suffix_token_id" },
+ { LLM_KV_TOKENIZER_MIDDLE_ID, "tokenizer.ggml.middle_token_id" },
+ { LLM_KV_TOKENIZER_EOT_ID, "tokenizer.ggml.eot_token_id" },
+
+ { LLM_KV_ADAPTER_TYPE, "adapter.type" },
+ { LLM_KV_ADAPTER_LORA_ALPHA, "adapter.lora.alpha" },
+};
+
+struct LLM_KV {
+ LLM_KV(llm_arch arch) : arch(arch) {}
+
+ llm_arch arch;
+
+ std::string operator()(llm_kv kv) const {
+ return ::format(LLM_KV_NAMES.at(kv), LLM_ARCH_NAMES.at(arch));
+ }
+};
+
+enum llm_tensor {
+ LLM_TENSOR_TOKEN_EMBD,
+ LLM_TENSOR_TOKEN_EMBD_NORM,
+ LLM_TENSOR_TOKEN_TYPES,
+ LLM_TENSOR_POS_EMBD,
+ LLM_TENSOR_OUTPUT,
+ LLM_TENSOR_OUTPUT_NORM,
+ LLM_TENSOR_ROPE_FREQS,
+ LLM_TENSOR_ROPE_FACTORS_LONG,
+ LLM_TENSOR_ROPE_FACTORS_SHORT,
+ LLM_TENSOR_ATTN_Q,
+ LLM_TENSOR_ATTN_K,
+ LLM_TENSOR_ATTN_V,
+ LLM_TENSOR_ATTN_QKV,
+ LLM_TENSOR_ATTN_OUT,
+ LLM_TENSOR_ATTN_NORM,
+ LLM_TENSOR_ATTN_NORM_2,
+ LLM_TENSOR_ATTN_OUT_NORM,
+ LLM_TENSOR_ATTN_POST_NORM,
+ LLM_TENSOR_ATTN_ROT_EMBD,
+ LLM_TENSOR_FFN_GATE_INP,
+ LLM_TENSOR_FFN_GATE_INP_SHEXP,
+ LLM_TENSOR_FFN_NORM,
+ LLM_TENSOR_FFN_POST_NORM,
+ LLM_TENSOR_FFN_GATE,
+ LLM_TENSOR_FFN_DOWN,
+ LLM_TENSOR_FFN_UP,
+ LLM_TENSOR_FFN_ACT,
+ LLM_TENSOR_FFN_DOWN_EXP, // split experts for backward compatibility
+ LLM_TENSOR_FFN_GATE_EXP,
+ LLM_TENSOR_FFN_UP_EXP,
+ LLM_TENSOR_FFN_NORM_EXPS,
+ LLM_TENSOR_FFN_DOWN_EXPS, // merged experts
+ LLM_TENSOR_FFN_GATE_EXPS,
+ LLM_TENSOR_FFN_UP_EXPS,
+ LLM_TENSOR_FFN_DOWN_SHEXP,
+ LLM_TENSOR_FFN_GATE_SHEXP,
+ LLM_TENSOR_FFN_UP_SHEXP,
+ LLM_TENSOR_ATTN_Q_NORM,
+ LLM_TENSOR_ATTN_K_NORM,
+ LLM_TENSOR_LAYER_OUT_NORM,
+ LLM_TENSOR_SSM_IN,
+ LLM_TENSOR_SSM_CONV1D,
+ LLM_TENSOR_SSM_X,
+ LLM_TENSOR_SSM_DT,
+ LLM_TENSOR_SSM_A,
+ LLM_TENSOR_SSM_D,
+ LLM_TENSOR_SSM_OUT,
+ LLM_TENSOR_ATTN_Q_A,
+ LLM_TENSOR_ATTN_Q_B,
+ LLM_TENSOR_ATTN_KV_A_MQA,
+ LLM_TENSOR_ATTN_KV_B,
+ LLM_TENSOR_ATTN_Q_A_NORM,
+ LLM_TENSOR_ATTN_KV_A_NORM,
+ LLM_TENSOR_ATTN_SUB_NORM,
+ LLM_TENSOR_FFN_SUB_NORM,
+ LLM_TENSOR_DEC_ATTN_NORM,
+ LLM_TENSOR_DEC_ATTN_Q,
+ LLM_TENSOR_DEC_ATTN_K,
+ LLM_TENSOR_DEC_ATTN_V,
+ LLM_TENSOR_DEC_ATTN_OUT,
+ LLM_TENSOR_DEC_ATTN_REL_B,
+ LLM_TENSOR_DEC_CROSS_ATTN_NORM,
+ LLM_TENSOR_DEC_CROSS_ATTN_Q,
+ LLM_TENSOR_DEC_CROSS_ATTN_K,
+ LLM_TENSOR_DEC_CROSS_ATTN_V,
+ LLM_TENSOR_DEC_CROSS_ATTN_OUT,
+ LLM_TENSOR_DEC_CROSS_ATTN_REL_B,
+ LLM_TENSOR_DEC_FFN_NORM,
+ LLM_TENSOR_DEC_FFN_GATE,
+ LLM_TENSOR_DEC_FFN_DOWN,
+ LLM_TENSOR_DEC_FFN_UP,
+ LLM_TENSOR_DEC_OUTPUT_NORM,
+ LLM_TENSOR_ENC_ATTN_NORM,
+ LLM_TENSOR_ENC_ATTN_Q,
+ LLM_TENSOR_ENC_ATTN_K,
+ LLM_TENSOR_ENC_ATTN_V,
+ LLM_TENSOR_ENC_ATTN_OUT,
+ LLM_TENSOR_ENC_ATTN_REL_B,
+ LLM_TENSOR_ENC_FFN_NORM,
+ LLM_TENSOR_ENC_FFN_GATE,
+ LLM_TENSOR_ENC_FFN_DOWN,
+ LLM_TENSOR_ENC_FFN_UP,
+ LLM_TENSOR_ENC_OUTPUT_NORM,
+};
+
+static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
+ {
+ LLM_ARCH_LLAMA,
+ {
+ { 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_GATE_INP, "blk.%d.ffn_gate_inp" },
+ { 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_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" },
+ { LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
+ { LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
+ { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
+ { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
+ { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
+ },
+ },
+ {
+ LLM_ARCH_BAICHUAN,
+ {
+ { 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_FALCON,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" },
+ { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ },
+ },
+ {
+ LLM_ARCH_GROK,
+ {
+ { 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_GATE_INP, "blk.%d.ffn_gate_inp" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" },
+ { LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
+ { LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
+ { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
+ { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
+ { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
+ { LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
+ { LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
+ },
+ },
+ {
+ LLM_ARCH_GPT2,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_POS_EMBD, "position_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ },
+ },
+ {
+ LLM_ARCH_GPTJ,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ },
+ },
+ {
+ LLM_ARCH_GPTNEOX,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ },
+ },
+ {
+ LLM_ARCH_MPT,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output"},
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ { LLM_TENSOR_FFN_ACT, "blk.%d.ffn.act" },
+ { LLM_TENSOR_POS_EMBD, "position_embd" },
+ { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm"},
+ { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm"},
+ },
+ },
+ {
+ LLM_ARCH_STARCODER,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_POS_EMBD, "position_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ },
+ },
+ {
+ LLM_ARCH_REFACT,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { 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_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_BERT,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
+ { LLM_TENSOR_TOKEN_TYPES, "token_types" },
+ { LLM_TENSOR_POS_EMBD, "position_embd" },
+ { LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_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_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ },
+ },
+ {
+ LLM_ARCH_NOMIC_BERT,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
+ { LLM_TENSOR_TOKEN_TYPES, "token_types" },
+ { LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
+ { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_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_JINA_BERT_V2,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
+ { LLM_TENSOR_TOKEN_TYPES, "token_types" },
+ { LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" },
+ { LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
+ { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
+ { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
+ { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
+ { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
+ { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ },
+ },
+ {
+ LLM_ARCH_BLOOM,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ },
+ },
+ {
+ LLM_ARCH_STABLELM,
+ {
+ { 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_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_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
+ { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
+ },
+ },
+ {
+ LLM_ARCH_QWEN,
+ {
+ { 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_QKV, "blk.%d.attn_qkv" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { 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_QWEN2,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { 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_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_QWEN2MOE,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { 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_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
+ { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
+ { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
+ { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
+ { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
+ { LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" },
+ { LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
+ { LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
+ },
+ },
+ {
+ LLM_ARCH_PHI2,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
+ { 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_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ },
+ },
+ {
+ LLM_ARCH_PHI3,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ROPE_FACTORS_LONG, "rope_factors_long" },
+ { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
+ { 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_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ },
+ },
+ {
+ LLM_ARCH_PLAMO,
+ {
+ { 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_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ },
+ },
+ {
+ LLM_ARCH_CODESHELL,
+ {
+ { 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_QKV, "blk.%d.attn_qkv" },
+ { 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_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_INTERNLM2,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { 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_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_MINICPM,
+ {
+ { 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_GATE_INP, "blk.%d.ffn_gate_inp" },
+ { 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_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" },
+ { LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" },
+ { LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" },
+ },
+ },
+ {
+ LLM_ARCH_GEMMA,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { 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_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_GEMMA2,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { 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_POST_NORM, "blk.%d.post_attention_norm" },
+ { 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_TENSOR_FFN_POST_NORM, "blk.%d.post_ffw_norm" },
+ },
+ },
+ {
+ LLM_ARCH_STARCODER2,
+ {
+ { 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_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ },
+ },
+ {
+ LLM_ARCH_MAMBA,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_SSM_IN, "blk.%d.ssm_in" },
+ { LLM_TENSOR_SSM_CONV1D, "blk.%d.ssm_conv1d" },
+ { LLM_TENSOR_SSM_X, "blk.%d.ssm_x" },
+ { LLM_TENSOR_SSM_DT, "blk.%d.ssm_dt" },
+ { LLM_TENSOR_SSM_A, "blk.%d.ssm_a" },
+ { LLM_TENSOR_SSM_D, "blk.%d.ssm_d" },
+ { LLM_TENSOR_SSM_OUT, "blk.%d.ssm_out" },
+ },
+ },
+ {
+ LLM_ARCH_XVERSE,
+ {
+ { 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_COMMAND_R,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { 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_FFN_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
+ { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
+ },
+ },
+ {
+ LLM_ARCH_DBRX,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
+ { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
+ { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
+ { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
+ { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
+ },
+ },
+ {
+ LLM_ARCH_OLMO,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { 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_FFN_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ },
+ },
+ {
+ LLM_ARCH_OPENELM,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
+ { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
+ { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { 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_ARCTIC,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { 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_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
+ { 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_TENSOR_FFN_NORM_EXPS, "blk.%d.ffn_norm_exps" },
+ { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
+ { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
+ { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
+ },
+ },
+ {
+ LLM_ARCH_DEEPSEEK2,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_Q_A_NORM, "blk.%d.attn_q_a_norm" },
+ { LLM_TENSOR_ATTN_KV_A_NORM, "blk.%d.attn_kv_a_norm" },
+ { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
+ { LLM_TENSOR_ATTN_Q_A, "blk.%d.attn_q_a" },
+ { LLM_TENSOR_ATTN_Q_B, "blk.%d.attn_q_b" },
+ { LLM_TENSOR_ATTN_KV_A_MQA, "blk.%d.attn_kv_a_mqa" },
+ { LLM_TENSOR_ATTN_KV_B, "blk.%d.attn_kv_b" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" },
+ { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" },
+ { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" },
+ { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" },
+ { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
+ { LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" },
+ { LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
+ { LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" },
+ },
+ },
+ {
+ LLM_ARCH_BITNET,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_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_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_SUB_NORM, "blk.%d.attn_sub_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_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_SUB_NORM, "blk.%d.ffn_sub_norm" },
+ },
+ },
+ {
+ LLM_ARCH_T5,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_DEC_OUTPUT_NORM, "dec.output_norm" },
+ { LLM_TENSOR_DEC_ATTN_NORM, "dec.blk.%d.attn_norm" },
+ { LLM_TENSOR_DEC_ATTN_Q, "dec.blk.%d.attn_q" },
+ { LLM_TENSOR_DEC_ATTN_K, "dec.blk.%d.attn_k" },
+ { LLM_TENSOR_DEC_ATTN_V, "dec.blk.%d.attn_v" },
+ { LLM_TENSOR_DEC_ATTN_OUT, "dec.blk.%d.attn_o" },
+ { LLM_TENSOR_DEC_ATTN_REL_B, "dec.blk.%d.attn_rel_b" },
+ { LLM_TENSOR_DEC_CROSS_ATTN_NORM, "dec.blk.%d.cross_attn_norm" },
+ { LLM_TENSOR_DEC_CROSS_ATTN_Q, "dec.blk.%d.cross_attn_q" },
+ { LLM_TENSOR_DEC_CROSS_ATTN_K, "dec.blk.%d.cross_attn_k" },
+ { LLM_TENSOR_DEC_CROSS_ATTN_V, "dec.blk.%d.cross_attn_v" },
+ { LLM_TENSOR_DEC_CROSS_ATTN_OUT, "dec.blk.%d.cross_attn_o" },
+ { LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "dec.blk.%d.cross_attn_rel_b" },
+ { LLM_TENSOR_DEC_FFN_NORM, "dec.blk.%d.ffn_norm" },
+ { LLM_TENSOR_DEC_FFN_GATE, "dec.blk.%d.ffn_gate" },
+ { LLM_TENSOR_DEC_FFN_DOWN, "dec.blk.%d.ffn_down" },
+ { LLM_TENSOR_DEC_FFN_UP, "dec.blk.%d.ffn_up" },
+ { LLM_TENSOR_ENC_OUTPUT_NORM, "enc.output_norm" },
+ { LLM_TENSOR_ENC_ATTN_NORM, "enc.blk.%d.attn_norm" },
+ { LLM_TENSOR_ENC_ATTN_Q, "enc.blk.%d.attn_q" },
+ { LLM_TENSOR_ENC_ATTN_K, "enc.blk.%d.attn_k" },
+ { LLM_TENSOR_ENC_ATTN_V, "enc.blk.%d.attn_v" },
+ { LLM_TENSOR_ENC_ATTN_OUT, "enc.blk.%d.attn_o" },
+ { LLM_TENSOR_ENC_ATTN_REL_B, "enc.blk.%d.attn_rel_b" },
+ { LLM_TENSOR_ENC_FFN_NORM, "enc.blk.%d.ffn_norm" },
+ { LLM_TENSOR_ENC_FFN_GATE, "enc.blk.%d.ffn_gate" },
+ { LLM_TENSOR_ENC_FFN_DOWN, "enc.blk.%d.ffn_down" },
+ { LLM_TENSOR_ENC_FFN_UP, "enc.blk.%d.ffn_up" },
+ },
+ },
+ {
+ LLM_ARCH_JAIS,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ { LLM_TENSOR_OUTPUT_NORM, "output_norm" },
+ { LLM_TENSOR_OUTPUT, "output" },
+ { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
+ { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" },
+ { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
+ { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
+ { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
+ { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
+ { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
+ },
+ },
+ {
+ LLM_ARCH_UNKNOWN,
+ {
+ { LLM_TENSOR_TOKEN_EMBD, "token_embd" },
+ },
+ },
+};
+
+static llm_arch llm_arch_from_string(const std::string & name) {
+ for (const auto & kv : LLM_ARCH_NAMES) { // NOLINT
+ if (kv.second == name) {
+ return kv.first;
+ }
+ }
+
+ return LLM_ARCH_UNKNOWN;
+}
+
+// helper to handle gguf constants
+// usage:
+//
+// const auto tn = LLM_TN(LLM_ARCH_LLAMA);
+//
+// std::string name = tn(LLM_TENSOR_OUTPUT); -> "output"
+// std::string name = tn(LLM_TENSOR_TOKEN_EMBD, "bias"); -> "token_embd.bias"
+// std::string name = tn(LLM_TENSOR_ATTN_NORM, "weight", 3); -> "blk.3.attn_norm.weight"
+//
+struct LLM_TN {
+ LLM_TN(llm_arch arch) : arch(arch) {}
+
+ llm_arch arch;
+
+ std::string operator()(llm_tensor tensor) const {
+ if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
+ return "__missing__";
+ }
+ return LLM_TENSOR_NAMES.at(arch).at(tensor);
+ }
+
+ std::string operator()(llm_tensor tensor, const std::string & suffix) const {
+ if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
+ return "__missing__";
+ }
+ return LLM_TENSOR_NAMES.at(arch).at(tensor) + "." + suffix;
+ }
+
+ std::string operator()(llm_tensor tensor, int bid) const {
+ if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
+ return "__missing__";
+ }
+ return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid);
+ }
+
+ std::string operator()(llm_tensor tensor, const std::string & suffix, int bid) const {
+ if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
+ return "__missing__";
+ }
+ return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid) + "." + suffix;
+ }
+
+ std::string operator()(llm_tensor tensor, const std::string & suffix, int bid, int xid) const {
+ if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
+ return "__missing__";
+ }
+ return ::format(LLM_TENSOR_NAMES.at(arch).at(tensor).c_str(), bid, xid) + "." + suffix;
+ }
+};
+
+//
+// gguf helpers
+//
+
+static const std::map<llama_rope_scaling_type, const char *> LLAMA_ROPE_SCALING_TYPES = {
+ { LLAMA_ROPE_SCALING_TYPE_NONE, "none" },
+ { LLAMA_ROPE_SCALING_TYPE_LINEAR, "linear" },
+ { LLAMA_ROPE_SCALING_TYPE_YARN, "yarn" },
+};
+
+static llama_rope_scaling_type llama_rope_scaling_type_from_string(const std::string & name) {
+ for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) {
+ if (kv.second == name) {
+ return (llama_rope_scaling_type) kv.first;
+ }
+ }
+
+ return LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
+}
+
+static std::string gguf_data_to_str(enum gguf_type type, const void * data, int i) {
+ switch (type) {
+ case GGUF_TYPE_UINT8: return std::to_string(((const uint8_t *)data)[i]);
+ case GGUF_TYPE_INT8: return std::to_string(((const int8_t *)data)[i]);
+ case GGUF_TYPE_UINT16: return std::to_string(((const uint16_t *)data)[i]);
+ case GGUF_TYPE_INT16: return std::to_string(((const int16_t *)data)[i]);
+ case GGUF_TYPE_UINT32: return std::to_string(((const uint32_t *)data)[i]);
+ case GGUF_TYPE_INT32: return std::to_string(((const int32_t *)data)[i]);
+ case GGUF_TYPE_UINT64: return std::to_string(((const uint64_t *)data)[i]);
+ case GGUF_TYPE_INT64: return std::to_string(((const int64_t *)data)[i]);
+ case GGUF_TYPE_FLOAT32: return std::to_string(((const float *)data)[i]);
+ case GGUF_TYPE_FLOAT64: return std::to_string(((const double *)data)[i]);
+ case GGUF_TYPE_BOOL: return ((const bool *)data)[i] ? "true" : "false";
+ default: return format("unknown type %d", type);
+ }
+}
+
+static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
+ const enum gguf_type type = gguf_get_kv_type(ctx_gguf, i);
+
+ switch (type) {
+ case GGUF_TYPE_STRING:
+ return gguf_get_val_str(ctx_gguf, i);
+ case GGUF_TYPE_ARRAY:
+ {
+ const enum gguf_type arr_type = gguf_get_arr_type(ctx_gguf, i);
+ int arr_n = gguf_get_arr_n(ctx_gguf, i);
+ const void * data = gguf_get_arr_data(ctx_gguf, i);
+ std::stringstream ss;
+ ss << "[";
+ for (int j = 0; j < arr_n; j++) {
+ if (arr_type == GGUF_TYPE_STRING) {
+ std::string val = gguf_get_arr_str(ctx_gguf, i, j);
+ // escape quotes
+ replace_all(val, "\\", "\\\\");
+ replace_all(val, "\"", "\\\"");
+ ss << '"' << val << '"';
+ } else if (arr_type == GGUF_TYPE_ARRAY) {
+ ss << "???";
+ } else {
+ ss << gguf_data_to_str(arr_type, data, j);
+ }
+ if (j < arr_n - 1) {
+ ss << ", ";
+ }
+ }
+ ss << "]";
+ return ss.str();
+ }
+ default:
+ return gguf_data_to_str(type, gguf_get_val_data(ctx_gguf, i), 0);
+ }
+}
+
+//
+// llama helpers
+//
+
+#if defined(_WIN32)
+static std::string llama_format_win_err(DWORD err) {
+ LPSTR buf;
+ size_t size = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
+ NULL, err, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&buf, 0, NULL);
+ if (!size) {
+ return "FormatMessageA failed";
+ }
+ std::string ret(buf, size);
+ LocalFree(buf);
+ return ret;
+}
+#endif
+
+template <typename T>
+struct no_init {
+ T value;
+ no_init() { /* do nothing */ }
+};
+
+struct llama_file {
+
+#if defined(_WIN32)
+ // use FILE * so we don't have to re-open the file to mmap
+ FILE * fp;
+ HANDLE fp_win32;
+ size_t size;
+
+private:
+ std::string GetErrorMessageWin32(DWORD error_code) const {
+ std::string ret;
+ LPSTR lpMsgBuf = NULL;
+ DWORD bufLen = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
+ NULL, error_code, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&lpMsgBuf, 0, NULL);
+ if (!bufLen) {
+ ret = format("Win32 error code: %s", error_code);
+ } else {
+ ret = lpMsgBuf;
+ LocalFree(lpMsgBuf);
+ }
+
+ return ret;
+ }
+
+public:
+
+ llama_file(const char * fname, const char * mode) {
+ fp = ggml_fopen(fname, mode);
+ if (fp == NULL) {
+ throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
+ }
+ fp_win32 = (HANDLE) _get_osfhandle(_fileno(fp));
+ seek(0, SEEK_END);
+ size = tell();
+ seek(0, SEEK_SET);
+ }
+
+ size_t tell() const {
+ // SetFilePointerEx returns the current position when seeking relative 0 bytes
+ LARGE_INTEGER li;
+ li.QuadPart = 0;
+ BOOL ret = SetFilePointerEx(fp_win32, li, &li, FILE_CURRENT);
+ if (!ret) {
+ throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
+ }
+
+ return li.QuadPart;
+ }
+
+ void seek(size_t offset, int whence) const {
+ // no need to convert SEEK_* to FILE_*. The enums are the same.
+ // Still, keep static asserts to avoid failures in the future.
+ static_assert(SEEK_SET == FILE_BEGIN, "SEEK_SET != FILE_BEGIN");
+ static_assert(SEEK_CUR == FILE_CURRENT, "SEEK_CUR != FILE_CURRENT");
+ static_assert(SEEK_END == FILE_END, "SEEK_END != FILE_END");
+
+ LARGE_INTEGER li;
+ li.QuadPart = offset;
+ BOOL ret = SetFilePointerEx(fp_win32, li, NULL, whence);
+ if (!ret) {
+ throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
+ }
+ }
+
+ void read_raw(void * ptr, size_t len) const {
+ // On Win32 ReadFile is significant faster than fread which is again significant faster than std::fstream. Thus
+ // use the Win32 API to do file io instead of the C/C++ library functions.
+
+ // There are conditions under which ReadFile cannot read chunks >64MB.
+ // Thus split the operation into smaller chunks if len exceeds this limit.
+ size_t bytes_read = 0;
+ while (bytes_read < len) {
+ size_t chunk_size = std::min<size_t>(len - bytes_read, 64*1024*1024);
+ DWORD chunk_read = 0;
+ BOOL result = ReadFile(fp_win32, reinterpret_cast<char*>(ptr) + bytes_read, chunk_size, &chunk_read, NULL);
+ if (!result) {
+ throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
+ }
+ if (chunk_read < chunk_size || chunk_read == 0) {
+ throw std::runtime_error("unexpectedly reached end of file");
+ }
+
+ bytes_read += chunk_read;
+ } ;
+ }
+
+ uint32_t read_u32() const {
+ uint32_t val;
+ read_raw(&val, sizeof(val));
+ return val;
+ }
+
+ void write_raw(const void * ptr, size_t len) const {
+ // There are conditions under which WriteFile cannot write chunks >64MB.
+ // Thus split the operation into smaller chunks if len exceeds this limit.
+ size_t bytes_written = 0;
+ while (bytes_written < len) {
+ size_t chunk_size = std::min<size_t>(len - bytes_written, 64*1024*1024);
+ DWORD chunk_written = 0;
+ BOOL result = WriteFile(fp_win32, reinterpret_cast<char const*>(ptr) + bytes_written, chunk_size, &chunk_written, NULL);
+ if (!result) {
+ throw std::runtime_error(format("write error: %s", GetErrorMessageWin32(GetLastError()).c_str()));
+ }
+ if (chunk_written < chunk_size || chunk_written == 0) {
+ throw std::runtime_error("unexpectedly failed to write bytes");
+ }
+
+ bytes_written += chunk_written;
+ }
+ }
+
+ void write_u32(std::uint32_t val) const {
+ write_raw(&val, sizeof(val));
+ }
+
+ ~llama_file() {
+ if (fp) {
+ std::fclose(fp);
+ }
+ }
+#else
+ // use FILE * so we don't have to re-open the file to mmap
+ FILE * fp;
+ size_t size;
+
+ llama_file(const char * fname, const char * mode) {
+ fp = ggml_fopen(fname, mode);
+ if (fp == NULL) {
+ throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
+ }
+ seek(0, SEEK_END);
+ size = tell();
+ seek(0, SEEK_SET);
+ }
+
+ size_t tell() const {
+#ifdef _WIN32
+ __int64 ret = _ftelli64(fp);
+#else
+ long ret = std::ftell(fp);
+#endif
+ if (ret == -1) {
+ throw std::runtime_error(format("ftell error: %s", strerror(errno)));
+ }
+
+ return (size_t) ret;
+ }
+
+ void seek(size_t offset, int whence) const {
+#ifdef _WIN32
+ int ret = _fseeki64(fp, (__int64) offset, whence);
+#else
+ int ret = std::fseek(fp, (long) offset, whence);
+#endif
+ if (ret != 0) {
+ throw std::runtime_error(format("seek error: %s", strerror(errno)));
+ }
+ }
+
+ void read_raw(void * ptr, size_t len) const {
+ if (len == 0) {
+ return;
+ }
+ errno = 0;
+ std::size_t ret = std::fread(ptr, len, 1, fp);
+ if (ferror(fp)) {
+ throw std::runtime_error(format("read error: %s", strerror(errno)));
+ }
+ if (ret != 1) {
+ throw std::runtime_error("unexpectedly reached end of file");
+ }
+ }
+
+ uint32_t read_u32() const {
+ uint32_t ret;
+ read_raw(&ret, sizeof(ret));
+ return ret;
+ }
+
+ void write_raw(const void * ptr, size_t len) const {
+ if (len == 0) {
+ return;
+ }
+ errno = 0;
+ size_t ret = std::fwrite(ptr, len, 1, fp);
+ if (ret != 1) {
+ throw std::runtime_error(format("write error: %s", strerror(errno)));
+ }
+ }
+
+ void write_u32(std::uint32_t val) const {
+ write_raw(&val, sizeof(val));
+ }
+
+ ~llama_file() {
+ if (fp) {
+ std::fclose(fp);
+ }
+ }
+#endif
+};
+using llama_files = std::vector<std::unique_ptr<llama_file>>;
+
+struct llama_mmap {
+ void * addr;
+ size_t size;
+
+ llama_mmap(const llama_mmap &) = delete;
+
+#ifdef _POSIX_MAPPED_FILES
+ static constexpr bool SUPPORTED = true;
+
+ // list of mapped fragments (first_offset, last_offset)
+ std::vector<std::pair<size_t, size_t>> mapped_fragments;
+
+ llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */, bool numa = false) {
+ size = file->size;
+ int fd = fileno(file->fp);
+ int flags = MAP_SHARED;
+ // prefetch/readahead impairs performance on NUMA systems
+ if (numa) { prefetch = 0; }
+#ifdef __linux__
+ // advise the kernel to read the file sequentially (increases readahead)
+ if (posix_fadvise(fd, 0, 0, POSIX_FADV_SEQUENTIAL)) {
+ LLAMA_LOG_WARN("warning: posix_fadvise(.., POSIX_FADV_SEQUENTIAL) failed: %s\n",
+ strerror(errno));
+ }
+ if (prefetch) { flags |= MAP_POPULATE; }
+#endif
+ addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
+ if (addr == MAP_FAILED) { // NOLINT
+ throw std::runtime_error(format("mmap failed: %s", strerror(errno)));
+ }
+
+ if (prefetch > 0) {
+ // advise the kernel to preload the mapped memory
+ if (posix_madvise(addr, std::min(file->size, prefetch), POSIX_MADV_WILLNEED)) {
+ LLAMA_LOG_WARN("warning: posix_madvise(.., POSIX_MADV_WILLNEED) failed: %s\n",
+ strerror(errno));
+ }
+ }
+ if (numa) {
+ // advise the kernel not to use readahead
+ // (because the next page might not belong on the same node)
+ if (posix_madvise(addr, file->size, POSIX_MADV_RANDOM)) {
+ LLAMA_LOG_WARN("warning: posix_madvise(.., POSIX_MADV_RANDOM) failed: %s\n",
+ strerror(errno));
+ }
+ }
+
+ // initialize list of mapped_fragments
+ mapped_fragments.emplace_back(0, file->size);
+ }
+
+ static void align_range(size_t * first, size_t * last, size_t page_size) {
+ // align first to the next page
+ size_t offset_in_page = *first & (page_size - 1);
+ size_t offset_to_page = offset_in_page == 0 ? 0 : page_size - offset_in_page;
+ *first += offset_to_page;
+
+ // align last to the previous page
+ *last = *last & ~(page_size - 1);
+
+ if (*last <= *first) {
+ *last = *first;
+ }
+ }
+
+ // partially unmap the file in the range [first, last)
+ void unmap_fragment(size_t first, size_t last) {
+ // note: this function must not be called multiple times with overlapping ranges
+ // otherwise, there is a risk of invalidating addresses that have been repurposed for other mappings
+ int page_size = sysconf(_SC_PAGESIZE);
+ align_range(&first, &last, page_size);
+ size_t len = last - first;
+
+ if (len == 0) {
+ return;
+ }
+
+ GGML_ASSERT(first % page_size == 0);
+ GGML_ASSERT(last % page_size == 0);
+ GGML_ASSERT(last > first);
+
+ void * next_page_start = (uint8_t *) addr + first;
+
+ // unmap the range
+ if (munmap(next_page_start, len)) {
+ LLAMA_LOG_WARN("warning: munmap failed: %s\n", strerror(errno));
+ }
+
+ // update the list of mapped fragments to avoid unmapping the same range again in the destructor
+ std::vector<std::pair<size_t, size_t>> new_mapped_fragments;
+ for (const auto & frag : mapped_fragments) {
+ if (frag.first < first && frag.second > last) {
+ // the range is in the middle of the fragment, split it
+ new_mapped_fragments.emplace_back(frag.first, first);
+ new_mapped_fragments.emplace_back(last, frag.second);
+ } else if (frag.first < first && frag.second > first) {
+ // the range starts in the middle of the fragment
+ new_mapped_fragments.emplace_back(frag.first, first);
+ } else if (frag.first < last && frag.second > last) {
+ // the range ends in the middle of the fragment
+ new_mapped_fragments.emplace_back(last, frag.second);
+ } else if (frag.first >= first && frag.second <= last) {
+ // the range covers the entire fragment
+ } else {
+ // the range is outside the fragment
+ new_mapped_fragments.push_back(frag);
+ }
+ }
+ mapped_fragments = std::move(new_mapped_fragments);
+ }
+
+ ~llama_mmap() {
+ for (const auto & frag : mapped_fragments) {
+ if (munmap((char *) addr + frag.first, frag.second - frag.first)) {
+ LLAMA_LOG_WARN("warning: munmap failed: %s\n", strerror(errno));
+ }
+ }
+ }
+#elif defined(_WIN32)
+ static constexpr bool SUPPORTED = true;
+
+ llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1, bool numa = false) {
+ GGML_UNUSED(numa);
+
+ size = file->size;
+
+ HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));
+
+ HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
+
+ if (hMapping == NULL) {
+ DWORD error = GetLastError();
+ throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
+ }
+
+ addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
+ DWORD error = GetLastError();
+ CloseHandle(hMapping);
+
+ if (addr == NULL) {
+ throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
+ }
+
+ if (prefetch > 0) {
+#if _WIN32_WINNT >= 0x602
+ // PrefetchVirtualMemory is only present on Windows 8 and above, so we dynamically load it
+ BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG);
+ HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll");
+
+ // may fail on pre-Windows 8 systems
+ pPrefetchVirtualMemory = reinterpret_cast<decltype(pPrefetchVirtualMemory)> (GetProcAddress(hKernel32, "PrefetchVirtualMemory"));
+
+ if (pPrefetchVirtualMemory) {
+ // advise the kernel to preload the mapped memory
+ WIN32_MEMORY_RANGE_ENTRY range;
+ range.VirtualAddress = addr;
+ range.NumberOfBytes = (SIZE_T) std::min(size, prefetch);
+ if (!pPrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
+ LLAMA_LOG_WARN("warning: PrefetchVirtualMemory failed: %s\n",
+ llama_format_win_err(GetLastError()).c_str());
+ }
+ }
+#else
+ throw std::runtime_error("PrefetchVirtualMemory unavailable");
+#endif
+ }
+ }
+
+ void unmap_fragment(size_t first, size_t last) {
+ // not supported
+ GGML_UNUSED(first);
+ GGML_UNUSED(last);
+ }
+
+ ~llama_mmap() {
+ if (!UnmapViewOfFile(addr)) {
+ LLAMA_LOG_WARN("warning: UnmapViewOfFile failed: %s\n",
+ llama_format_win_err(GetLastError()).c_str());
+ }
+ }
+#else
+ static constexpr bool SUPPORTED = false;
+
+ llama_mmap(struct llama_file * file, size_t prefetch = -1, bool numa = false) {
+ GGML_UNUSED(file);
+ GGML_UNUSED(prefetch);
+ GGML_UNUSED(numa);
+
+ throw std::runtime_error("mmap not supported");
+ }
+
+ void unmap_fragment(size_t first, size_t last) {
+ GGML_UNUSED(first);
+ GGML_UNUSED(last);
+
+ throw std::runtime_error("mmap not supported");
+ }
+#endif
+};
+using llama_mmaps = std::vector<std::unique_ptr<llama_mmap>>;
+
+// Represents some region of memory being locked using mlock or VirtualLock;
+// will automatically unlock on destruction.
+struct llama_mlock {
+ void * addr = NULL;
+ size_t size = 0;
+
+ bool failed_already = false;
+
+ llama_mlock() {}
+ llama_mlock(const llama_mlock &) = delete;
+
+ ~llama_mlock() {
+ if (size) {
+ raw_unlock(addr, size);
+ }
+ }
+
+ void init(void * ptr) {
+ GGML_ASSERT(addr == NULL && size == 0); // NOLINT
+ addr = ptr;
+ }
+
+ void grow_to(size_t target_size) {
+ GGML_ASSERT(addr);
+ if (failed_already) {
+ return;
+ }
+ size_t granularity = lock_granularity();
+ target_size = (target_size + granularity - 1) & ~(granularity - 1);
+ if (target_size > size) {
+ if (raw_lock((uint8_t *) addr + size, target_size - size)) {
+ size = target_size;
+ } else {
+ failed_already = true;
+ }
+ }
+ }
+
+#ifdef _POSIX_MEMLOCK_RANGE
+ static constexpr bool SUPPORTED = true;
+
+ static size_t lock_granularity() {
+ return (size_t) sysconf(_SC_PAGESIZE);
+ }
+
+ #ifdef __APPLE__
+ #define MLOCK_SUGGESTION \
+ "Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or " \
+ "decreasing 'vm.global_no_user_wire_amount'. Also try increasing RLIMIT_MEMLOCK (ulimit -l).\n"
+ #else
+ #define MLOCK_SUGGESTION \
+ "Try increasing RLIMIT_MEMLOCK ('ulimit -l' as root).\n"
+ #endif
+
+ bool raw_lock(const void * addr, size_t size) const {
+ if (!mlock(addr, size)) {
+ return true;
+ }
+
+ char* errmsg = std::strerror(errno);
+ bool suggest = (errno == ENOMEM);
+
+ // Check if the resource limit is fine after all
+ struct rlimit lock_limit;
+ if (suggest && getrlimit(RLIMIT_MEMLOCK, &lock_limit)) {
+ suggest = false;
+ }
+ if (suggest && (lock_limit.rlim_max > lock_limit.rlim_cur + size)) {
+ suggest = false;
+ }
+
+ LLAMA_LOG_WARN("warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s",
+ size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : "");
+ return false;
+ }
+
+ #undef MLOCK_SUGGESTION
+
+ static void raw_unlock(void * addr, size_t size) {
+ if (munlock(addr, size)) {
+ LLAMA_LOG_WARN("warning: failed to munlock buffer: %s\n", std::strerror(errno));
+ }
+ }
+#elif defined(_WIN32)
+ static constexpr bool SUPPORTED = true;
+
+ static size_t lock_granularity() {
+ SYSTEM_INFO si;
+ GetSystemInfo(&si);
+ return (size_t) si.dwPageSize;
+ }
+
+ bool raw_lock(void * ptr, size_t len) const {
+ for (int tries = 1; ; tries++) {
+ if (VirtualLock(ptr, len)) {
+ return true;
+ }
+ if (tries == 2) {
+ LLAMA_LOG_WARN("warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n",
+ len, size, llama_format_win_err(GetLastError()).c_str());
+ return false;
+ }
+
+ // It failed but this was only the first try; increase the working
+ // set size and try again.
+ SIZE_T min_ws_size, max_ws_size;
+ if (!GetProcessWorkingSetSize(GetCurrentProcess(), &min_ws_size, &max_ws_size)) {
+ LLAMA_LOG_WARN("warning: GetProcessWorkingSetSize failed: %s\n",
+ llama_format_win_err(GetLastError()).c_str());
+ return false;
+ }
+ // Per MSDN: "The maximum number of pages that a process can lock
+ // is equal to the number of pages in its minimum working set minus
+ // a small overhead."
+ // Hopefully a megabyte is enough overhead:
+ size_t increment = len + 1048576;
+ // The minimum must be <= the maximum, so we need to increase both:
+ min_ws_size += increment;
+ max_ws_size += increment;
+ if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
+ LLAMA_LOG_WARN("warning: SetProcessWorkingSetSize failed: %s\n",
+ llama_format_win_err(GetLastError()).c_str());
+ return false;
+ }
+ }
+ }
+
+ static void raw_unlock(void * ptr, size_t len) {
+ if (!VirtualUnlock(ptr, len)) {
+ LLAMA_LOG_WARN("warning: failed to VirtualUnlock buffer: %s\n",
+ llama_format_win_err(GetLastError()).c_str());
+ }
+ }
+#else
+ static constexpr bool SUPPORTED = false;
+
+ static size_t lock_granularity() {
+ return (size_t) 65536;
+ }
+
+ bool raw_lock(const void * addr, size_t len) const {
+ LLAMA_LOG_WARN("warning: mlock not supported on this system\n");
+ return false;
+ }
+
+ static void raw_unlock(const void * addr, size_t len) {}
+#endif
+};
+using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
+
+// NOTE: avoid ever using this except for building the token_to_piece caches
+static std::string llama_token_to_piece(const struct llama_model * model, llama_token token, bool special) {
+ std::string piece;
+ piece.resize(piece.capacity()); // using string internal cache
+ const int n_chars = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special);
+ if (n_chars < 0) {
+ piece.resize(-n_chars);
+ int check = llama_token_to_piece(model, token, &piece[0], piece.size(), 0, special);
+ GGML_ASSERT(check == -n_chars);
+ }
+ else {
+ piece.resize(n_chars);
+ }
+
+ return piece;
+}
+
+static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer) {
+ ggml_backend_buffer_type_t buft = nullptr;
+
+#if defined(GGML_USE_CUDA)
+ // host buffers should only be used when data is expected to be copied to/from the GPU
+ if (host_buffer) {
+ buft = ggml_backend_cuda_host_buffer_type();
+ }
+#elif defined(GGML_USE_SYCL)
+ if (host_buffer) {
+ 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_cpu_buffer_type();
+ }
+ return buft;
+
+ GGML_UNUSED(host_buffer);
+}
+
+//
+// globals
+//
+
+struct llama_state {
+ llama_state() {
+#ifdef GGML_USE_METAL
+ ggml_backend_metal_log_set_callback(log_callback, log_callback_user_data);
+#elif defined(GGML_USE_CUDA)
+ ggml_backend_cuda_log_set_callback(log_callback, log_callback_user_data);
+#elif defined(GGML_USE_CANN)
+ ggml_backend_cann_log_set_callback(log_callback, log_callback_user_data);
+#endif
+ }
+
+ // We save the log callback globally
+ ggml_log_callback log_callback = llama_log_callback_default;
+ void * log_callback_user_data = nullptr;
+};
+
+static llama_state g_state;
+
+// available llama models
+enum e_model {
+ MODEL_UNKNOWN,
+ MODEL_14M,
+ MODEL_17M,
+ MODEL_22M,
+ MODEL_33M,
+ MODEL_60M,
+ MODEL_70M,
+ MODEL_80M,
+ MODEL_109M,
+ MODEL_137M,
+ MODEL_160M,
+ MODEL_220M,
+ MODEL_250M,
+ MODEL_270M,
+ MODEL_335M,
+ MODEL_410M,
+ MODEL_450M,
+ MODEL_770M,
+ MODEL_780M,
+ MODEL_0_5B,
+ MODEL_1B,
+ MODEL_1_3B,
+ MODEL_1_4B,
+ MODEL_2B,
+ MODEL_2_8B,
+ MODEL_3B,
+ MODEL_4B,
+ MODEL_6B,
+ MODEL_6_9B,
+ MODEL_7B,
+ MODEL_8B,
+ MODEL_9B,
+ MODEL_11B,
+ MODEL_12B,
+ MODEL_13B,
+ MODEL_14B,
+ MODEL_15B,
+ MODEL_16B,
+ MODEL_20B,
+ MODEL_30B,
+ MODEL_34B,
+ MODEL_35B,
+ MODEL_40B,
+ MODEL_65B,
+ MODEL_70B,
+ MODEL_236B,
+ MODEL_314B,
+ MODEL_SMALL,
+ MODEL_MEDIUM,
+ MODEL_LARGE,
+ MODEL_XL,
+ MODEL_A2_7B,
+ MODEL_8x7B,
+ MODEL_8x22B,
+ MODEL_16x12B,
+ MODEL_10B_128x3_66B,
+ MODEL_57B_A14B,
+ MODEL_27B,
+};
+
+static const size_t kiB = 1024;
+static const size_t MiB = 1024*kiB;
+static const size_t GiB = 1024*MiB;
+
+struct llama_hparams {
+ bool vocab_only;
+ bool rope_finetuned;
+ bool use_par_res;
+
+ uint32_t n_vocab;
+ uint32_t n_ctx_train; // context size the model was trained on
+ uint32_t n_embd;
+ uint32_t n_layer;
+ uint32_t n_rot;
+ uint32_t n_swa = 0; // sliding window attention (SWA)
+ uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
+ uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
+ uint32_t n_expert = 0;
+ uint32_t n_expert_used = 0;
+ uint32_t n_vocab_type = 0; // for BERT-style token types
+ uint32_t n_rel_attn_bkts = 0;
+
+ std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_arr;
+ std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_kv_arr;
+ std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
+
+ uint32_t n_layer_dense_lead = 0;
+ uint32_t n_lora_q = 0;
+ uint32_t n_lora_kv = 0;
+ uint32_t n_ff_exp = 0;
+ uint32_t n_ff_shexp = 0;
+ uint32_t n_expert_shared = 0;
+ float expert_weights_scale = 0.0;
+
+ float f_norm_eps;
+ float f_norm_rms_eps;
+
+ float f_attn_logit_softcapping = 50.0f;
+ float f_final_logit_softcapping = 30.0f;
+
+ float rope_attn_factor = 1.0f;
+ float rope_freq_base_train;
+ float rope_freq_scale_train;
+ uint32_t n_ctx_orig_yarn;
+ float rope_yarn_log_mul;
+
+ // for State Space Models
+ uint32_t ssm_d_conv = 0;
+ uint32_t ssm_d_inner = 0;
+ uint32_t ssm_d_state = 0;
+ uint32_t ssm_dt_rank = 0;
+
+ float f_clamp_kqv = 0.0f;
+ float f_max_alibi_bias = 0.0f;
+ float f_logit_scale = 0.0f;
+
+ bool causal_attn = true;
+ bool use_alibi = false;
+ bool attn_soft_cap = false;
+
+ // needed by encoder-decoder models (e.g. T5, FLAN-T5)
+ // ref: https://github.com/ggerganov/llama.cpp/pull/8141
+ llama_token dec_start_token_id = -1;
+
+ enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_NONE;
+ enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE;
+ enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
+
+ bool operator!=(const llama_hparams & other) const {
+ if (this->vocab_only != other.vocab_only) return true;
+ if (this->n_vocab != other.n_vocab) return true;
+ if (this->n_ctx_train != other.n_ctx_train) return true;
+ if (this->n_embd != other.n_embd) return true;
+ if (this->n_layer != other.n_layer) return true;
+ if (this->n_rot != other.n_rot) return true;
+ if (this->n_swa != other.n_swa) return true;
+ if (this->n_embd_head_k != other.n_embd_head_k) return true;
+ if (this->n_embd_head_v != other.n_embd_head_v) return true;
+ if (this->n_expert != other.n_expert) return true;
+ if (this->n_expert_used != other.n_expert_used) return true;
+
+ if (this->n_head_arr != other.n_head_arr) return true;
+ if (this->n_head_kv_arr != other.n_head_kv_arr) return true;
+ if (this->n_ff_arr != other.n_ff_arr) return true;
+
+ if (this->n_rel_attn_bkts != other.n_rel_attn_bkts) return true;
+ if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true;
+ if (this->n_lora_q != other.n_lora_q) return true;
+ if (this->n_lora_kv != other.n_lora_kv) return true;
+ if (this->n_ff_exp != other.n_ff_exp) return true;
+ if (this->n_ff_shexp != other.n_ff_shexp) return true;
+ if (this->n_expert_shared != other.n_expert_shared) return true;
+
+ if (this->rope_finetuned != other.rope_finetuned) return true;
+ if (this->n_ctx_orig_yarn != other.n_ctx_orig_yarn) return true;
+
+ if (this->ssm_d_conv != other.ssm_d_conv) return true;
+ if (this->ssm_d_inner != other.ssm_d_inner) return true;
+ if (this->ssm_d_state != other.ssm_d_state) return true;
+ if (this->ssm_dt_rank != other.ssm_dt_rank) return true;
+
+ if (this->dec_start_token_id != other.dec_start_token_id) return true;
+
+ const float EPSILON = 1e-9f;
+
+ if (!is_float_close(this->f_norm_eps, other.f_norm_eps, EPSILON)) return true;
+ if (!is_float_close(this->f_norm_rms_eps, other.f_norm_rms_eps, EPSILON)) return true;
+ if (!is_float_close(this->rope_attn_factor, other.rope_attn_factor, EPSILON)) return true;
+ if (!is_float_close(this->rope_freq_base_train, other.rope_freq_base_train, EPSILON)) return true;
+ if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true;
+ if (!is_float_close(this->expert_weights_scale, other.expert_weights_scale, EPSILON)) return true;
+ if (!is_float_close(this->rope_yarn_log_mul, other.rope_yarn_log_mul, EPSILON)) return true;
+
+ return false;
+ }
+
+ uint32_t n_head(uint32_t il = 0) const {
+ if (il < n_layer) {
+ return n_head_arr[il];
+ }
+
+ GGML_ASSERT(false);
+ return 0;
+ }
+
+ uint32_t n_head_kv(uint32_t il = 0) const {
+ if (il < n_layer) {
+ return n_head_kv_arr[il];
+ }
+
+ GGML_ASSERT(false);
+ return 0;
+ }
+
+ uint32_t n_ff(uint32_t il = 0) const {
+ if (il < n_layer) {
+ return n_ff_arr[il];
+ }
+
+ GGML_ASSERT(false);
+ return 0;
+ }
+
+ uint32_t n_gqa(uint32_t il = 0) const {
+ const uint32_t n_head = this->n_head(il);
+ const uint32_t n_head_kv = this->n_head_kv(il);
+
+ if (n_head_kv == 0) {
+ return 0;
+ }
+
+ return n_head/n_head_kv;
+ }
+
+ uint32_t n_embd_k_gqa(uint32_t il = 0) const { // dimension of key embeddings across all k-v heads
+ const uint32_t n_head_kv = this->n_head_kv(il);
+
+ return n_embd_head_k * n_head_kv;
+ }
+
+ uint32_t n_embd_v_gqa(uint32_t il = 0) const { // dimension of value embeddings across all k-v heads
+ const uint32_t n_head_kv = this->n_head_kv(il);
+
+ return n_embd_head_v * n_head_kv;
+ }
+
+ uint32_t n_embd_k_s() const { // dimension of the rolling state embeddings
+ // corresponds to Mamba's conv_states size
+ // TODO: maybe support other convolution strides than 1
+ // NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
+ return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner;
+ }
+
+ uint32_t n_embd_v_s() const { // dimension of the recurrent state embeddings
+ // corresponds to Mamba's ssm_states size
+ return ssm_d_state * ssm_d_inner;
+ }
+};
+
+static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
+
+struct llama_cparams {
+ uint32_t n_ctx; // context size used during inference
+ uint32_t n_batch;
+ uint32_t n_ubatch;
+ uint32_t n_seq_max;
+ uint32_t n_threads; // number of threads to use for generation
+ uint32_t n_threads_batch; // number of threads to use for batch processing
+
+ float rope_freq_base;
+ float rope_freq_scale;
+
+ uint32_t n_ctx_orig_yarn;
+ // These hyperparameters are not exposed in GGUF, because all
+ // existing YaRN models use the same values for them.
+ float yarn_ext_factor;
+ float yarn_attn_factor;
+ float yarn_beta_fast;
+ float yarn_beta_slow;
+ float defrag_thold;
+
+ bool embeddings;
+ bool causal_attn;
+ bool offload_kqv;
+ bool flash_attn;
+
+ enum llama_pooling_type pooling_type;
+
+ ggml_backend_sched_eval_callback cb_eval;
+ void * cb_eval_user_data;
+};
+
+// TODO: separate into "llama_layer_enc" and "llama_layer_dec"
+struct llama_layer {
+ // normalization
+ struct ggml_tensor * attn_norm;
+ struct ggml_tensor * attn_norm_b;
+ struct ggml_tensor * attn_norm_2;
+ struct ggml_tensor * attn_norm_2_b;
+ struct ggml_tensor * attn_q_norm;
+ struct ggml_tensor * attn_q_norm_b;
+ struct ggml_tensor * attn_k_norm;
+ struct ggml_tensor * attn_k_norm_b;
+ struct ggml_tensor * attn_out_norm;
+ struct ggml_tensor * attn_out_norm_b;
+ struct ggml_tensor * attn_q_a_norm;
+ struct ggml_tensor * attn_kv_a_norm;
+ struct ggml_tensor * attn_sub_norm;
+ struct ggml_tensor * attn_post_norm;
+ struct ggml_tensor * ffn_sub_norm;
+ struct ggml_tensor * attn_norm_cross;
+ struct ggml_tensor * attn_norm_enc;
+
+ // attention
+ struct ggml_tensor * wq;
+ struct ggml_tensor * wk;
+ struct ggml_tensor * wv;
+ struct ggml_tensor * wo;
+ struct ggml_tensor * wqkv;
+ struct ggml_tensor * wq_a;
+ struct ggml_tensor * wq_b;
+ struct ggml_tensor * wkv_a_mqa;
+ struct ggml_tensor * wkv_b;
+ struct ggml_tensor * wq_cross;
+ struct ggml_tensor * wk_cross;
+ struct ggml_tensor * wv_cross;
+ struct ggml_tensor * wo_cross;
+ struct ggml_tensor * wq_enc;
+ struct ggml_tensor * wk_enc;
+ struct ggml_tensor * wv_enc;
+ struct ggml_tensor * wo_enc;
+
+ // attention bias
+ struct ggml_tensor * bq;
+ struct ggml_tensor * bk;
+ struct ggml_tensor * bv;
+ struct ggml_tensor * bo;
+ struct ggml_tensor * bqkv;
+
+ // relative position bias
+ struct ggml_tensor * attn_rel_b;
+ struct ggml_tensor * attn_rel_b_enc;
+ struct ggml_tensor * attn_rel_b_cross;
+
+ // normalization
+ struct ggml_tensor * ffn_norm;
+ struct ggml_tensor * ffn_norm_b;
+ struct ggml_tensor * ffn_post_norm;
+ struct ggml_tensor * layer_out_norm;
+ struct ggml_tensor * layer_out_norm_b;
+ struct ggml_tensor * ffn_norm_exps;
+ struct ggml_tensor * ffn_norm_enc;
+
+ // ff
+ struct ggml_tensor * ffn_gate; // w1
+ struct ggml_tensor * ffn_down; // w2
+ struct ggml_tensor * ffn_up; // w3
+ struct ggml_tensor * ffn_gate_enc;
+ struct ggml_tensor * ffn_down_enc;
+ struct ggml_tensor * ffn_up_enc;
+
+ // ff MoE
+ struct ggml_tensor * ffn_gate_inp;
+ struct ggml_tensor * ffn_gate_exps;
+ struct ggml_tensor * ffn_down_exps;
+ struct ggml_tensor * ffn_up_exps ;
+
+ // ff shared expert (shexp)
+ struct ggml_tensor * ffn_gate_inp_shexp;
+ struct ggml_tensor * ffn_gate_shexp;
+ struct ggml_tensor * ffn_down_shexp;
+ struct ggml_tensor * ffn_up_shexp;
+
+ // ff bias
+ struct ggml_tensor * ffn_gate_b = nullptr;
+ struct ggml_tensor * ffn_down_b = nullptr; // b2
+ struct ggml_tensor * ffn_up_b = nullptr; // b3
+ struct ggml_tensor * ffn_act;
+
+ // mamba proj
+ struct ggml_tensor * ssm_in;
+ struct ggml_tensor * ssm_x;
+ struct ggml_tensor * ssm_dt;
+ struct ggml_tensor * ssm_out;
+
+ // mamba
+ struct ggml_tensor * ssm_conv1d;
+ struct ggml_tensor * ssm_a;
+ struct ggml_tensor * ssm_d;
+
+ // mamba bias
+ struct ggml_tensor * ssm_conv1d_b;
+ struct ggml_tensor * ssm_dt_b;
+
+ // long rope factors
+ struct ggml_tensor * rope_long = nullptr;
+ struct ggml_tensor * rope_short = nullptr;
+
+ // bitnet scale
+ struct ggml_tensor * wq_scale;
+ struct ggml_tensor * wk_scale;
+ struct ggml_tensor * wv_scale;
+ struct ggml_tensor * wo_scale;
+ struct ggml_tensor * ffn_gate_scale;
+ struct ggml_tensor * ffn_up_scale;
+ struct ggml_tensor * ffn_down_scale;
+};
+
+struct llama_kv_cell {
+ llama_pos pos = -1;
+ llama_pos delta = 0;
+ int32_t src = 0; // used by recurrent state models to copy states
+
+ std::set<llama_seq_id> seq_id;
+
+ bool has_seq_id(const llama_seq_id & id) const {
+ return seq_id.find(id) != seq_id.end();
+ }
+
+ bool is_empty() const {
+ return seq_id.empty();
+ }
+
+ bool is_same_seq(const llama_kv_cell & other) const {
+ return seq_id == other.seq_id;
+ }
+};
+
+// ring-buffer of cached KV data
+struct llama_kv_cache {
+ bool has_shift = false;
+ bool do_defrag = false;
+ bool do_copy = false;
+ bool recurrent = false; // with recurrent state models, a cell can hold the state for more than one past token
+ bool v_trans = true; // the value tensor is transposed
+
+ // Note: The value of head isn't only used to optimize searching
+ // for a free KV slot. llama_decode_internal also uses it, so it
+ // cannot be freely changed after a slot has been allocated.
+ uint32_t head = 0;
+ uint32_t size = 0;
+ uint32_t used = 0; // used cells (i.e. at least one seq_id)
+
+ // computed before each graph build
+ uint32_t n = 0;
+
+ ggml_type type_k = GGML_TYPE_F16;
+ ggml_type type_v = GGML_TYPE_F16;
+
+ std::vector<llama_kv_cell> cells;
+
+ std::vector<struct ggml_tensor *> k_l; // per layer
+ std::vector<struct ggml_tensor *> v_l;
+
+ std::vector<struct ggml_context *> ctxs;
+ std::vector<ggml_backend_buffer_t> bufs;
+
+ size_t total_size() const {
+ size_t size = 0;
+ for (ggml_backend_buffer_t buf : bufs) {
+ size += ggml_backend_buffer_get_size(buf);
+ }
+ return size;
+ }
+
+ ~llama_kv_cache() {
+ for (struct ggml_context * ctx : ctxs) {
+ ggml_free(ctx);
+ }
+ for (ggml_backend_buffer_t buf : bufs) {
+ ggml_backend_buffer_free(buf);
+ }
+ }
+};
+
+struct llama_control_vector {
+ std::vector<struct ggml_tensor *> tensors; // per layer
+ std::vector<struct ggml_context *> ctxs;
+ std::vector<ggml_backend_buffer_t> bufs;
+
+ int32_t layer_start = -1;
+ int32_t layer_end = -1;
+
+ struct ggml_tensor * tensor_for(int il) const {
+ if (il < 0 || il < layer_start || il > layer_end || (size_t) il >= tensors.size()) {
+ return nullptr;
+ }
+ return tensors[il];
+ }
+
+ struct ggml_tensor * apply_to(struct ggml_context * ctx, struct ggml_tensor * cur, int il) const {
+ ggml_tensor * layer_dir = tensor_for(il);
+ if (layer_dir != nullptr) {
+ cur = ggml_add(ctx, cur, layer_dir);
+ }
+ return cur;
+ }
+
+ ~llama_control_vector() {
+ for (struct ggml_context * ctx : ctxs) {
+ ggml_free(ctx);
+ }
+ for (ggml_backend_buffer_t buf : bufs) {
+ ggml_backend_buffer_free(buf);
+ }
+ }
+};
+
+struct llama_model {
+ e_model type = MODEL_UNKNOWN;
+ llm_arch arch = LLM_ARCH_UNKNOWN;
+ llama_ftype ftype = LLAMA_FTYPE_ALL_F32;
+
+ std::string name = "n/a";
+
+ llama_hparams hparams = {};
+ llama_vocab vocab;
+
+ struct ggml_tensor * tok_embd;
+ struct ggml_tensor * type_embd;
+ struct ggml_tensor * pos_embd;
+ struct ggml_tensor * tok_norm;
+ struct ggml_tensor * tok_norm_b;
+
+ struct ggml_tensor * output_norm;
+ struct ggml_tensor * output_norm_b;
+ struct ggml_tensor * output;
+ struct ggml_tensor * output_b;
+ struct ggml_tensor * output_norm_enc;
+
+ std::vector<llama_layer> layers;
+
+ llama_split_mode split_mode;
+ int main_gpu;
+ int n_gpu_layers;
+
+ std::vector<std::string> rpc_servers;
+
+ // gguf metadata
+ std::unordered_map<std::string, std::string> gguf_kv;
+
+ // layer -> buffer type mapping
+ struct layer_buft {
+ layer_buft() : buft_matrix(nullptr), buft(nullptr) {}
+ layer_buft(ggml_backend_buffer_type_t matrix) : buft_matrix(matrix), buft(matrix) {}
+ layer_buft(ggml_backend_buffer_type_t matrix, ggml_backend_buffer_type_t other) : buft_matrix(matrix), buft(other) {}
+
+ ggml_backend_buffer_type_t buft_matrix; // matrices only - used by split buffers and backends that support only matrix multiplication
+ ggml_backend_buffer_type_t buft; // everything else
+ };
+
+ layer_buft buft_input;
+ layer_buft buft_output;
+ std::vector<layer_buft> buft_layer;
+
+ // contexts where the model tensors metadata is stored
+ std::vector<struct ggml_context *> ctxs;
+
+ // the model memory buffers for the tensor data
+ std::vector<ggml_backend_buffer_t> bufs;
+
+ // model memory mapped files
+ llama_mmaps mappings;
+
+ // objects representing data potentially being locked in memory
+ llama_mlocks mlock_bufs;
+ llama_mlocks mlock_mmaps;
+
+ // for quantize-stats only
+ std::vector<std::pair<std::string, struct ggml_tensor *>> tensors_by_name;
+
+ int64_t t_load_us = 0;
+ int64_t t_start_us = 0;
+
+ // keep track of loaded lora adapters
+ std::set<struct llama_lora_adapter *> lora_adapters;
+
+ ~llama_model() {
+ for (struct ggml_context * ctx : ctxs) {
+ ggml_free(ctx);
+ }
+ for (ggml_backend_buffer_t buf : bufs) {
+#ifdef GGML_USE_CUDA
+ if (ggml_backend_buffer_get_type(buf) == ggml_backend_cpu_buffer_type()) {
+ ggml_backend_cuda_unregister_host_buffer(ggml_backend_buffer_get_base(buf));
+ }
+#endif
+ ggml_backend_buffer_free(buf);
+ }
+ while (!lora_adapters.empty()) {
+ llama_lora_adapter_free(*lora_adapters.begin());
+ }
+ }
+};
+
+struct llama_context {
+ llama_context(const llama_model & model)
+ : model(model)
+ , sampling(llama_n_vocab(&model))
+ , t_start_us(model.t_start_us)
+ , t_load_us(model.t_load_us) {}
+
+ ~llama_context() {
+ ggml_backend_sched_free(sched);
+
+ for (ggml_backend_t backend : backends) {
+ ggml_backend_free(backend);
+ }
+
+ ggml_backend_buffer_free(buf_output);
+ }
+
+ const struct llama_model & model;
+
+ struct llama_cparams cparams;
+ struct llama_sampling sampling;
+ struct llama_kv_cache kv_self;
+ struct llama_control_vector cvec;
+
+ std::unordered_map<struct llama_lora_adapter *, float> lora_adapters;
+
+ std::vector<ggml_backend_t> backends;
+#ifdef GGML_USE_METAL
+ ggml_backend_t backend_metal = nullptr;
+#endif
+#ifdef GGML_USE_BLAS
+ ggml_backend_t backend_blas = nullptr;
+#endif
+ ggml_backend_t backend_cpu = nullptr;
+
+ bool has_evaluated_once = false;
+
+ int64_t t_start_us;
+ int64_t t_load_us;
+ int64_t t_p_eval_us = 0;
+ int64_t t_eval_us = 0;
+
+ int64_t t_compute_start_us = 0;
+ int64_t n_queued_tokens = 0;
+
+ int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
+ int32_t n_eval = 0; // number of eval calls
+
+ // host buffer for the model output (logits and embeddings)
+ ggml_backend_buffer_t buf_output = nullptr;
+
+ // decode output (2-dimensional array: [n_outputs][n_vocab])
+ size_t logits_size = 0; // capacity (of floats) for logits
+ float * logits = nullptr;
+
+ std::vector<int32_t> output_ids; // map batch token positions to ids of the logits and embd buffers
+ size_t output_size = 0; // capacity (of tokens positions) for the output buffers
+ int32_t n_outputs = 0; // number of actually-used outputs in the current ubatch or last logical batch
+
+ bool logits_all = false;
+
+ // embeddings output (2-dimensional array: [n_outputs][n_embd])
+ // populated only when pooling_type == LLAMA_POOLING_TYPE_NONE
+ size_t embd_size = 0; // capacity (of floats) for embeddings
+ float * embd = nullptr;
+
+ // sequence embeddings output (map of [n_embd] vectors)
+ // populated only when pooling_type != LLAMA_POOLING_TYPE_NONE
+ std::map<llama_seq_id, std::vector<float>> embd_seq;
+
+ // whether we are computing encoder output or decoder output
+ bool is_encoding = false;
+
+ // output of the encoder part of the encoder-decoder models
+ std::vector<float> embd_enc;
+ std::vector<std::set<llama_seq_id>> seq_ids_enc;
+
+ // memory buffers used to evaluate the model
+ std::vector<uint8_t> buf_compute_meta;
+ ggml_backend_sched_t sched = nullptr;
+
+ ggml_abort_callback abort_callback = nullptr;
+ void * abort_callback_data = nullptr;
+
+ // input tensors
+ struct ggml_tensor * inp_tokens; // I32 [n_batch]
+ struct ggml_tensor * inp_embd; // F32 [n_embd, n_batch]
+ struct ggml_tensor * inp_pos; // I32 [n_batch]
+ struct ggml_tensor * inp_out_ids; // I32 [n_outputs]
+ struct ggml_tensor * inp_KQ_mask; // F32 [kv_size, n_batch]
+ struct ggml_tensor * inp_KQ_mask_swa; // F32 [kv_size, n_batch]
+ struct ggml_tensor * inp_K_shift; // I32 [kv_size]
+ struct ggml_tensor * inp_mean; // F32 [n_batch, n_batch]
+ struct ggml_tensor * inp_cls; // I32 [n_batch]
+ struct ggml_tensor * inp_s_copy; // I32 [kv_size]
+ struct ggml_tensor * inp_s_mask; // F32 [1, n_kv]
+ struct ggml_tensor * inp_s_seq; // I32 [n_kv, n_batch]
+ struct ggml_tensor * inp_pos_bucket; // I32 [n_batch|n_kv, n_batch]
+ struct ggml_tensor * inp_embd_enc; // F32 [n_embd, n_outputs_enc]
+ struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
+};
+
+struct llama_lora_weight {
+ struct ggml_tensor * a = nullptr;
+ struct ggml_tensor * b = nullptr;
+ llama_lora_weight() = default;
+ llama_lora_weight(struct ggml_tensor * a, struct ggml_tensor * b): a(a), b(b) {}
+};
+
+struct llama_lora_adapter {
+ struct llama_model * base_model;
+ // map tensor name to lora_a_b
+ std::unordered_map<std::string, struct llama_lora_weight> ab_map;
+ std::vector<struct ggml_context *> ctxs;
+ std::vector<ggml_backend_buffer_t> bufs;
+
+ float alpha;
+
+ llama_lora_adapter(struct llama_model * base_model): base_model(base_model) {
+ base_model->lora_adapters.insert(this);
+ }
+
+ llama_lora_weight * get_weight(struct ggml_tensor * w) {
+ std::string name(w->name);
+ auto pos = ab_map.find(name);
+ if (ab_map.find(name) != ab_map.end()) {
+ return &pos->second;
+ }
+ return nullptr;
+ }
+
+ ~llama_lora_adapter() {
+ for (struct ggml_context * ctx : ctxs) {
+ ggml_free(ctx);
+ }
+ for (ggml_backend_buffer_t buf : bufs) {
+ ggml_backend_buffer_free(buf);
+ }
+ auto pos = base_model->lora_adapters.find(this);
+ if (pos != base_model->lora_adapters.end()) {
+ base_model->lora_adapters.erase(pos);
+ }
+ }
+};
+
+static size_t llama_get_device_count(const llama_model & model) {
+ size_t count = 1;
+#if defined(GGML_USE_CUDA)
+ count = ggml_backend_cuda_get_device_count();
+#elif defined(GGML_USE_SYCL)
+ count = ggml_backend_sycl_get_device_count();
+#elif defined(GGML_USE_VULKAN)
+ count = ggml_backend_vk_get_device_count();
+#elif defined(GGML_USE_CANN)
+ return ggml_backend_cann_get_device_count();
+#endif
+#if defined(GGML_USE_RPC)
+ count += model.rpc_servers.size();
+#endif
+ return count;
+ GGML_UNUSED(model);
+}
+
+static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_model & model, int gpu) {
+ ggml_backend_buffer_type_t buft = nullptr;
+
+#if defined(GGML_USE_RPC)
+ int dev_count = (int)llama_get_device_count(model);
+ int rpc_count = (int)model.rpc_servers.size();
+ if (gpu >= dev_count - rpc_count) {
+ const char * endpoint = model.rpc_servers[gpu - dev_count + rpc_count].c_str();
+ return ggml_backend_rpc_buffer_type(endpoint);
+ }
+#endif
+#if defined(GGML_USE_METAL)
+ buft = ggml_backend_metal_buffer_type();
+#elif defined(GGML_USE_CUDA)
+ buft = ggml_backend_cuda_buffer_type(gpu);
+#elif defined(GGML_USE_VULKAN)
+ buft = ggml_backend_vk_buffer_type(gpu);
+#elif defined(GGML_USE_SYCL)
+ buft = ggml_backend_sycl_buffer_type(gpu);
+#elif defined(GGML_USE_KOMPUTE)
+ buft = ggml_backend_kompute_buffer_type(gpu);
+ if (buft == nullptr) {
+ LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu);
+ }
+#elif defined(GGML_USE_CANN)
+ buft = ggml_backend_cann_buffer_type(gpu);
+#endif
+
+ if (buft == nullptr) {
+ buft = llama_default_buffer_type_cpu(true);
+ }
+ return buft;
+ GGML_UNUSED(model);
+ GGML_UNUSED(gpu);
+}
+
+static ggml_backend_buffer_type_t llama_default_buffer_type_split(const llama_model & model, int fallback_gpu, const float * tensor_split) {
+ ggml_backend_buffer_type_t buft = nullptr;
+
+#ifdef GGML_USE_CUDA
+ if (ggml_backend_cuda_get_device_count() > 1) {
+ buft = ggml_backend_cuda_split_buffer_type(tensor_split);
+ }
+#endif
+
+#ifdef GGML_USE_SYCL
+ if (ggml_backend_sycl_get_device_count() > 1) {
+ buft = ggml_backend_sycl_split_buffer_type(tensor_split);
+ }
+#endif
+
+ if (buft == nullptr) {
+ buft = llama_default_buffer_type_offload(model, fallback_gpu);
+ }
+ return buft;
+
+ GGML_UNUSED(tensor_split);
+}
+
+static size_t llama_get_device_memory(const llama_model & model, int device) {
+#if defined(GGML_USE_RPC)
+ int dev_count = (int)llama_get_device_count(model);
+ int rpc_count = (int)model.rpc_servers.size();
+ if (device >= dev_count - rpc_count) {
+ size_t total;
+ size_t free;
+ const char * endpoint = model.rpc_servers[device - dev_count + rpc_count].c_str();
+ ggml_backend_rpc_get_device_memory(endpoint, &free, &total);
+ return free;
+ }
+#endif
+#if defined(GGML_USE_CUDA)
+ size_t total;
+ size_t free;
+ ggml_backend_cuda_get_device_memory(device, &free, &total);
+ return free;
+#elif defined(GGML_USE_SYCL)
+ size_t total;
+ size_t free;
+ ggml_backend_sycl_get_device_memory(device, &free, &total);
+ return free;
+#elif defined(GGML_USE_VULKAN)
+ size_t total;
+ size_t free;
+ ggml_backend_vk_get_device_memory(device, &free, &total);
+ return free;
+#elif defined(GGML_USE_CANN)
+ size_t total;
+ size_t free;
+ ggml_backend_cann_get_device_memory(device, &free, &total);
+ return free;
+#else
+ return 1;
+#endif
+ GGML_UNUSED(model);
+ GGML_UNUSED(device);
+}
+
+//
+// kv cache helpers
+//
+
+static bool llama_kv_cache_init(
+ struct llama_kv_cache & cache,
+ const llama_context * ctx,
+ ggml_type type_k,
+ ggml_type type_v,
+ uint32_t kv_size,
+ bool offload) {
+ const llama_model & model = ctx->model;
+ const llama_cparams & cparams = ctx->cparams;
+
+ const struct llama_hparams & hparams = model.hparams;
+
+ const int64_t n_layer = hparams.n_layer;
+
+ cache.has_shift = false;
+
+ // TODO: find a nicer way to add other recurrent model architectures
+ cache.recurrent = model.arch == LLM_ARCH_MAMBA;
+ cache.v_trans = !cparams.flash_attn;
+
+ cache.head = 0;
+ cache.size = kv_size;
+ cache.used = 0;
+
+ cache.type_k = type_k;
+ cache.type_v = type_v;
+
+ cache.cells.clear();
+ cache.cells.resize(kv_size);
+
+ if (cache.recurrent) {
+ // init state copy sources
+ for (uint32_t i = 0; i < cache.size; ++i) {
+ cache.cells[i].src = i;
+ }
+ }
+
+ // count used buffer types
+ std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
+ if (offload) {
+ for (int64_t i = 0; i < n_layer; ++i) {
+ buft_layer_count[model.buft_layer[i].buft]++;
+ }
+ } else {
+ buft_layer_count[llama_default_buffer_type_cpu(true)] = n_layer;
+ }
+
+ // create a context for each buffer type
+ std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
+ for (auto & it : buft_layer_count) {
+ int n_layers = it.second;
+ struct ggml_init_params params = {
+ /*.mem_size =*/ 2u*n_layers*ggml_tensor_overhead(),
+ /*.mem_buffer =*/ NULL,
+ /*.no_alloc =*/ true,
+ };
+ ggml_context * ctx = ggml_init(params);
+ if (!ctx) {
+ LLAMA_LOG_ERROR("%s: failed to allocate context for kv cache\n", __func__);
+ return false;
+ }
+ ctx_map[it.first] = ctx;
+ cache.ctxs.push_back(ctx);
+ }
+
+ cache.k_l.reserve(n_layer);
+ cache.v_l.reserve(n_layer);
+
+ for (int i = 0; i < (int) n_layer; i++) {
+ const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(i) + hparams.n_embd_k_s();
+ const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(i) + hparams.n_embd_v_s();
+
+ struct ggml_context * ctx = offload ? ctx_map.at(model.buft_layer[i].buft) : cache.ctxs.front();
+ ggml_tensor * k = ggml_new_tensor_1d(ctx, type_k, n_embd_k_gqa*kv_size);
+ ggml_tensor * v = ggml_new_tensor_1d(ctx, type_v, n_embd_v_gqa*kv_size);
+ ggml_format_name(k, "cache_k_l%d", i);
+ ggml_format_name(v, "cache_v_l%d", i);
+ cache.k_l.push_back(k);
+ cache.v_l.push_back(v);
+ }
+
+ // allocate tensors and initialize the buffers to avoid NaNs in the padding
+ for (auto it : ctx_map) {
+ ggml_backend_buffer_type_t buft = it.first;
+ ggml_context * ctx = it.second;
+ ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
+ if (!buf) {
+ LLAMA_LOG_ERROR("%s: failed to allocate buffer for kv cache\n", __func__);
+ return false;
+ }
+ ggml_backend_buffer_clear(buf, 0);
+ LLAMA_LOG_INFO("%s: %10s KV buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0);
+ cache.bufs.push_back(buf);
+ }
+
+ return true;
+}
+
+// find an empty slot of size "n_tokens" in the cache
+// updates the cache head
+// Note: On success, it's important that cache.head points
+// to the first cell of the slot.
+static bool llama_kv_cache_find_slot(
+ struct llama_kv_cache & cache,
+ const struct llama_batch & batch) {
+ const uint32_t n_tokens = batch.n_tokens;
+
+ if (cache.recurrent) {
+ // For recurrent state architectures (like Mamba),
+ // each KV cache cell can store the state for a whole sequence.
+
+ llama_seq_id min = cache.size - 1;
+ llama_seq_id max = 0;
+
+ for (uint32_t i = 0; i < n_tokens; ++i) {
+ for (int32_t j = 0; j < batch.n_seq_id[i]; ++j) {
+ llama_seq_id seq_id = batch.seq_id[i][j];
+ // make sure it's a valid seq_id
+ if ((uint32_t) seq_id < cache.size) {
+ if (seq_id > max) {
+ max = seq_id;
+ }
+ if (seq_id < min) {
+ min = seq_id;
+ }
+ // Assuming the tokens are in-order
+ if (batch.pos[i] != cache.cells[seq_id].pos + 1) {
+ // What should happen when the pos backtracks or skips a value?
+ // Clearing the state mid-batch would require special-casing which isn't done.
+ LLAMA_LOG_WARN("%s: non-consecutive token position %d after %d for sequence %d\n",
+ __func__, batch.pos[i], cache.cells[seq_id].pos, seq_id);
+ }
+ if (cache.cells[seq_id].pos < 0 && 0 <= batch.pos[i]) {
+ cache.used += 1;
+ }
+ cache.cells[seq_id].pos = batch.pos[i];
+ // NOTE: seq_ids are not inserted here; they are handled when the input tensors are set
+ } else {
+ // too big seq_id
+ // TODO: would it be possible to resize the KV cache size instead?
+ LLAMA_LOG_ERROR("%s: seq_id=%d >= kv_size=%d Try using a bigger --parallel value\n", __func__, seq_id, cache.size);
+ return false;
+ }
+ }
+ }
+
+ // allow getting the range of used cells, from head to head + n
+ cache.head = min;
+ cache.n = max - min + 1;
+
+ // sanity check
+ return max >= min;
+ }
+ // otherwise, one cell per token.
+
+ if (n_tokens > cache.size) {
+ LLAMA_LOG_ERROR("%s: n_tokens=%d > cache.size=%d\n", __func__, n_tokens, cache.size);
+ return false;
+ }
+
+ uint32_t n_tested = 0;
+
+ while (true) {
+ if (cache.head + n_tokens > cache.size) {
+ n_tested += cache.size - cache.head;
+ cache.head = 0;
+ continue;
+ }
+
+ bool found = true;
+ for (uint32_t i = 0; i < n_tokens; i++) {
+ if (cache.cells[cache.head + i].pos >= 0) {
+ found = false;
+ cache.head += i + 1;
+ n_tested += i + 1;
+ break;
+ }
+ }
+
+ if (found) {
+ break;
+ }
+
+ if (n_tested >= cache.size) {
+ //LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
+ return false;
+ }
+ }
+
+ for (uint32_t i = 0; i < n_tokens; i++) {
+ cache.cells[cache.head + i].pos = batch.pos[i];
+
+ for (int32_t j = 0; j < batch.n_seq_id[i]; j++) {
+ cache.cells[cache.head + i].seq_id.insert(batch.seq_id[i][j]);
+ }
+ }
+
+ cache.used += n_tokens;
+
+ return true;
+}
+
+// find how many cells are currently in use
+static uint32_t llama_kv_cache_cell_max(const struct llama_kv_cache & cache) {
+ for (uint32_t i = cache.size; i > 0; --i) {
+ const llama_kv_cell & cell = cache.cells[i - 1];
+
+ if (cell.pos >= 0 && !cell.is_empty()) {
+ return i;
+ }
+ }
+
+ return 0;
+}
+
+static void llama_kv_cache_clear(struct llama_kv_cache & cache) {
+ for (int32_t i = 0; i < (int32_t) cache.size; ++i) {
+ cache.cells[i].pos = -1;
+ cache.cells[i].seq_id.clear();
+ }
+ cache.head = 0;
+ cache.used = 0;
+
+ for (auto & buf : cache.bufs) {
+ ggml_backend_buffer_clear(buf, 0);
+ }
+}
+
+static bool llama_kv_cache_seq_rm(
+ struct llama_kv_cache & cache,
+ llama_seq_id seq_id,
+ llama_pos p0,
+ llama_pos p1) {
+ uint32_t new_head = cache.size;
+
+ if (p0 < 0) p0 = 0;
+ if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
+
+ // models like Mamba can't have a state partially erased
+ if (cache.recurrent) {
+ if (seq_id >= (int64_t) cache.size) {
+ // could be fatal
+ return false;
+ }
+ if (0 <= seq_id) {
+ // partial intersection is invalid
+ if ((0 < p0 && p0 <= cache.cells[seq_id].pos) || (0 < p1 && p1 <= cache.cells[seq_id].pos)) {
+ return false;
+ }
+ } else {
+ // seq_id is negative, then the range should include everything or nothing
+ if (p0 != p1 && (p0 != 0 || p1 != std::numeric_limits<llama_pos>::max())) {
+ return false;
+ }
+ }
+ }
+
+ for (uint32_t i = 0; i < cache.size; ++i) {
+ if (cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
+ if (seq_id < 0) {
+ cache.cells[i].seq_id.clear();
+ } else if (cache.cells[i].has_seq_id(seq_id)) {
+ cache.cells[i].seq_id.erase(seq_id);
+ } else {
+ continue;
+ }
+ if (cache.cells[i].is_empty()) {
+ // keep count of the number of used cells
+ if (cache.cells[i].pos >= 0) cache.used--;
+
+ cache.cells[i].pos = -1;
+ if (new_head == cache.size) new_head = i;
+ }
+ }
+ }
+
+ // If we freed up a slot, set head to it so searching can start there.
+ if (new_head != cache.size && new_head < cache.head) cache.head = new_head;
+
+ return true;
+}
+
+static void llama_kv_cache_seq_cp(
+ struct llama_kv_cache & cache,
+ llama_seq_id seq_id_src,
+ llama_seq_id seq_id_dst,
+ llama_pos p0,
+ llama_pos p1) {
+ if (p0 < 0) p0 = 0;
+ if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
+
+ if (cache.recurrent) {
+ if ((uint32_t) seq_id_dst < cache.size && (uint32_t) seq_id_src < cache.size) {
+ seq_id_src = cache.cells[seq_id_src].src;
+ GGML_ASSERT((uint32_t) seq_id_src < cache.size);
+ // intent to "copy from"
+ // supports copy chains thanks to taking the source of the source
+ cache.cells[seq_id_dst].src = seq_id_src;
+
+ // preserve the "keep or clear" status of the copied sequence
+ if (cache.cells[seq_id_src].has_seq_id(seq_id_src)) {
+ cache.cells[seq_id_dst].seq_id.insert(seq_id_dst);
+ } else {
+ cache.cells[seq_id_dst].seq_id.erase(seq_id_dst);
+ }
+
+ cache.do_copy = true;
+
+ cache.cells[seq_id_dst].pos = cache.cells[seq_id_src].pos;
+ }
+ return;
+ }
+ // otherwise, this is the KV cache of a Transformer-like model
+
+ cache.head = 0;
+
+ for (uint32_t i = 0; i < cache.size; ++i) {
+ if (cache.cells[i].has_seq_id(seq_id_src) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
+ cache.cells[i].seq_id.insert(seq_id_dst);
+ }
+ }
+}
+
+static void llama_kv_cache_seq_keep(struct llama_kv_cache & cache, llama_seq_id seq_id) {
+ uint32_t new_head = cache.size;
+
+ for (uint32_t i = 0; i < cache.size; ++i) {
+ if (!cache.cells[i].has_seq_id(seq_id)) {
+ if (cache.cells[i].pos >= 0) cache.used--;
+ cache.cells[i].pos = -1;
+ cache.cells[i].seq_id.clear();
+ if (new_head == cache.size) new_head = i;
+ } else {
+ cache.cells[i].seq_id.clear();
+ cache.cells[i].seq_id.insert(seq_id);
+ }
+ }
+
+ // If we freed up a slot, set head to it so searching can start there.
+ if (new_head != cache.size && new_head < cache.head) cache.head = new_head;
+}
+
+static void llama_kv_cache_seq_add(
+ struct llama_kv_cache & cache,
+ llama_seq_id seq_id,
+ llama_pos p0,
+ llama_pos p1,
+ llama_pos delta) {
+ uint32_t new_head = cache.size;
+
+ if (p0 < 0) p0 = 0;
+ if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
+ // If there is no range then return early to avoid looping over the cache.
+ if (p0 == p1) return;
+
+ if (cache.recurrent) {
+ // for Mamba-like models, only the pos needs to be shifted
+ if (0 <= seq_id && seq_id < (int64_t) cache.size) {
+ llama_kv_cell & cell = cache.cells[seq_id];
+ if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
+ cell.pos += delta;
+ }
+ }
+ return;
+ }
+
+ for (uint32_t i = 0; i < cache.size; ++i) {
+ if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
+ cache.has_shift = true;
+ cache.cells[i].pos += delta;
+ cache.cells[i].delta += delta;
+
+ if (cache.cells[i].pos < 0) {
+ if (!cache.cells[i].is_empty()) {
+ cache.used--;
+ }
+ cache.cells[i].pos = -1;
+ cache.cells[i].seq_id.clear();
+ if (new_head == cache.size) {
+ new_head = i;
+ }
+ }
+ }
+ }
+
+ // If we freed up a slot, set head to it so searching can start there.
+ // Otherwise we just start the next search from the beginning.
+ cache.head = new_head != cache.size ? new_head : 0;
+}
+
+static void llama_kv_cache_seq_div(
+ struct llama_kv_cache & cache,
+ llama_seq_id seq_id,
+ llama_pos p0,
+ llama_pos p1,
+ int d) {
+ if (p0 < 0) p0 = 0;
+ if (p1 < 0) p1 = std::numeric_limits<llama_pos>::max();
+ // If there is no range then return early to avoid looping over the cache.
+ if (p0 == p1) return;
+
+ if (cache.recurrent) {
+ // for Mamba-like models, only the pos needs to be changed
+ if (0 <= seq_id && seq_id < (int64_t) cache.size) {
+ llama_kv_cell & cell = cache.cells[seq_id];
+ if (cell.has_seq_id(seq_id) && p0 <= cell.pos && cell.pos < p1) {
+ cell.pos /= d;
+ }
+ }
+ return;
+ }
+
+ for (uint32_t i = 0; i < cache.size; ++i) {
+ if (cache.cells[i].has_seq_id(seq_id) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
+ cache.has_shift = true;
+
+ {
+ llama_pos p_old = cache.cells[i].pos;
+ cache.cells[i].pos /= d;
+ cache.cells[i].delta += cache.cells[i].pos - p_old;
+ }
+ }
+ }
+}
+
+static llama_pos llama_kv_cache_seq_pos_max(struct llama_kv_cache & cache, llama_seq_id seq_id) {
+ llama_pos result = 0;
+
+ for (uint32_t i = 0; i < cache.size; ++i) {
+ if (cache.cells[i].has_seq_id(seq_id)) {
+ result = std::max(result, cache.cells[i].pos);
+ }
+ }
+
+ return result;
+}
+
+static void llama_kv_cache_defrag(struct llama_kv_cache & cache) {
+ cache.do_defrag = true;
+}
+
+static uint32_t llama_kv_cache_get_padding(const struct llama_cparams & cparams) {
+ // the FA kernels require padding to avoid extra runtime boundary checks
+ return cparams.flash_attn ? 256u : 32u;
+}
+
+//
+// model loading and saving
+//
+
+enum llama_fver {
+ GGUF_FILE_VERSION_V1 = 1,
+ GGUF_FILE_VERSION_V2 = 2,
+ GGUF_FILE_VERSION_V3 = 3,
+};
+
+static const char * llama_file_version_name(llama_fver version) {
+ switch (version) {
+ case GGUF_FILE_VERSION_V1: return "GGUF V1 (support until nov 2023)";
+ case GGUF_FILE_VERSION_V2: return "GGUF V2";
+ case GGUF_FILE_VERSION_V3: return "GGUF V3 (latest)";
+ }
+
+ return "unknown";
+}
+
+static std::string llama_format_tensor_shape(const std::vector<int64_t> & ne) {
+ char buf[256];
+ snprintf(buf, sizeof(buf), "%5" PRId64, ne.at(0));
+ for (size_t i = 1; i < ne.size(); i++) {
+ snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, ne.at(i));
+ }
+ return buf;
+}
+
+static std::string llama_format_tensor_shape(const struct ggml_tensor * t) {
+ char buf[256];
+ snprintf(buf, sizeof(buf), "%5" PRId64, t->ne[0]);
+ for (int i = 1; i < GGML_MAX_DIMS; i++) {
+ snprintf(buf + strlen(buf), sizeof(buf) - strlen(buf), ", %5" PRId64, t->ne[i]);
+ }
+ return buf;
+}
+
+namespace GGUFMeta {
+ template <typename T, gguf_type gt_, T (*gfun)(const gguf_context *, const int)>
+ struct GKV_Base_Type {
+ static constexpr gguf_type gt = gt_;
+
+ static T getter(const gguf_context * ctx, const int kid) {
+ return gfun(ctx, kid);
+ }
+ };
+
+ template<typename T> struct GKV_Base;
+
+ template<> struct GKV_Base<bool >: GKV_Base_Type<bool, GGUF_TYPE_BOOL, gguf_get_val_bool> {};
+ template<> struct GKV_Base<uint8_t >: GKV_Base_Type<uint8_t, GGUF_TYPE_UINT8, gguf_get_val_u8 > {};
+ template<> struct GKV_Base<uint16_t >: GKV_Base_Type<uint16_t, GGUF_TYPE_UINT16, gguf_get_val_u16 > {};
+ template<> struct GKV_Base<uint32_t >: GKV_Base_Type<uint32_t, GGUF_TYPE_UINT32, gguf_get_val_u32 > {};
+ template<> struct GKV_Base<uint64_t >: GKV_Base_Type<uint64_t, GGUF_TYPE_UINT64, gguf_get_val_u64 > {};
+ template<> struct GKV_Base<int8_t >: GKV_Base_Type<int8_t, GGUF_TYPE_INT8, gguf_get_val_i8 > {};
+ template<> struct GKV_Base<int16_t >: GKV_Base_Type<int16_t, GGUF_TYPE_INT16, gguf_get_val_i16 > {};
+ template<> struct GKV_Base<int32_t >: GKV_Base_Type<int32_t, GGUF_TYPE_INT32, gguf_get_val_i32 > {};
+ template<> struct GKV_Base<int64_t >: GKV_Base_Type<int64_t, GGUF_TYPE_INT64, gguf_get_val_i64 > {};
+ template<> struct GKV_Base<float >: GKV_Base_Type<float, GGUF_TYPE_FLOAT32, gguf_get_val_f32 > {};
+ template<> struct GKV_Base<double >: GKV_Base_Type<double, GGUF_TYPE_FLOAT64, gguf_get_val_f64 > {};
+ template<> struct GKV_Base<const char *>: GKV_Base_Type<const char *, GGUF_TYPE_STRING, gguf_get_val_str > {};
+
+ template<> struct GKV_Base<std::string> {
+ static constexpr gguf_type gt = GGUF_TYPE_STRING;
+
+ static std::string getter(const gguf_context * ctx, const int kid) {
+ return gguf_get_val_str(ctx, kid);
+ }
+ };
+
+ struct ArrayInfo {
+ const gguf_type gt;
+ const size_t length;
+ const void * data;
+ };
+
+ template<> struct GKV_Base<ArrayInfo> {
+ public:
+ static constexpr gguf_type gt = GGUF_TYPE_ARRAY;
+ static ArrayInfo getter(const gguf_context *ctx, const int k) {
+ return ArrayInfo {
+ gguf_get_arr_type(ctx, k),
+ size_t(gguf_get_arr_n(ctx, k)),
+ gguf_get_arr_data(ctx, k),
+ };
+ }
+ };
+
+ template<typename T>
+ class GKV : public GKV_Base<T> {
+ GKV() = delete;
+
+ public:
+ static T get_kv(const gguf_context * ctx, const int k) {
+ const enum gguf_type kt = gguf_get_kv_type(ctx, k);
+
+ if (kt != GKV::gt) {
+ throw std::runtime_error(format("key %s has wrong type %s but expected type %s",
+ gguf_get_key(ctx, k), gguf_type_name(kt), gguf_type_name(GKV::gt)));
+ }
+ return GKV::getter(ctx, k);
+ }
+
+ static const char * override_type_to_str(const llama_model_kv_override_type ty) {
+ switch (ty) {
+ case LLAMA_KV_OVERRIDE_TYPE_BOOL: return "bool";
+ case LLAMA_KV_OVERRIDE_TYPE_INT: return "int";
+ case LLAMA_KV_OVERRIDE_TYPE_FLOAT: return "float";
+ case LLAMA_KV_OVERRIDE_TYPE_STR: return "str";
+ }
+ return "unknown";
+ }
+
+ static bool validate_override(const llama_model_kv_override_type expected_type, const struct llama_model_kv_override * ovrd) {
+ if (!ovrd) { return false; }
+ if (ovrd->tag == expected_type) {
+ LLAMA_LOG_INFO("%s: Using metadata override (%5s) '%s' = ",
+ __func__, override_type_to_str(ovrd->tag), ovrd->key);
+ switch (ovrd->tag) {
+ case LLAMA_KV_OVERRIDE_TYPE_BOOL: {
+ LLAMA_LOG_INFO("%s\n", ovrd->val_bool ? "true" : "false");
+ } break;
+ case LLAMA_KV_OVERRIDE_TYPE_INT: {
+ LLAMA_LOG_INFO("%" PRId64 "\n", ovrd->val_i64);
+ } break;
+ case LLAMA_KV_OVERRIDE_TYPE_FLOAT: {
+ LLAMA_LOG_INFO("%.6f\n", ovrd->val_f64);
+ } break;
+ case LLAMA_KV_OVERRIDE_TYPE_STR: {
+ LLAMA_LOG_INFO("%s\n", ovrd->val_str);
+ } break;
+ default:
+ // Shouldn't be possible to end up here, but just in case...
+ throw std::runtime_error(
+ format("Unsupported attempt to override %s type for metadata key %s\n",
+ override_type_to_str(ovrd->tag), ovrd->key));
+ }
+ return true;
+ }
+ LLAMA_LOG_WARN("%s: Warning: Bad metadata override type for key '%s', expected %s but got %s\n",
+ __func__, ovrd->key, override_type_to_str(expected_type), override_type_to_str(ovrd->tag));
+ return false;
+ }
+
+ template<typename OT>
+ static typename std::enable_if<std::is_same<OT, bool>::value, bool>::type
+ try_override(OT & target, const struct llama_model_kv_override * ovrd) {
+ if (validate_override(LLAMA_KV_OVERRIDE_TYPE_BOOL, ovrd)) {
+ target = ovrd->val_bool;
+ return true;
+ }
+ return false;
+ }
+
+ template<typename OT>
+ static typename std::enable_if<!std::is_same<OT, bool>::value && std::is_integral<OT>::value, bool>::type
+ try_override(OT & target, const struct llama_model_kv_override * ovrd) {
+ if (validate_override(LLAMA_KV_OVERRIDE_TYPE_INT, ovrd)) {
+ target = ovrd->val_i64;
+ return true;
+ }
+ return false;
+ }
+
+ template<typename OT>
+ static typename std::enable_if<std::is_floating_point<OT>::value, bool>::type
+ try_override(T & target, const struct llama_model_kv_override * ovrd) {
+ if (validate_override(LLAMA_KV_OVERRIDE_TYPE_FLOAT, ovrd)) {
+ target = ovrd->val_f64;
+ return true;
+ }
+ return false;
+ }
+
+ template<typename OT>
+ static typename std::enable_if<std::is_same<OT, std::string>::value, bool>::type
+ try_override(T & target, const struct llama_model_kv_override * ovrd) {
+ if (validate_override(LLAMA_KV_OVERRIDE_TYPE_STR, ovrd)) {
+ target = ovrd->val_str;
+ return true;
+ }
+ return false;
+ }
+
+ static bool set(const gguf_context * ctx, const int k, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
+ if (try_override<T>(target, ovrd)) {
+ return true;
+ }
+ if (k < 0) { return false; }
+ target = get_kv(ctx, k);
+ return true;
+ }
+
+ static bool set(const gguf_context * ctx, const char * key, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
+ return set(ctx, gguf_find_key(ctx, key), target, ovrd);
+ }
+
+ static bool set(const gguf_context * ctx, const std::string & key, T & target, const struct llama_model_kv_override * ovrd = nullptr) {
+ return set(ctx, key.c_str(), target, ovrd);
+ }
+ };
+}
+
+using llama_buf_map = std::unordered_map<uint32_t, ggml_backend_buffer_t>;
+
+struct llama_model_loader {
+ int n_kv = 0;
+ int n_tensors = 0;
+ int n_created = 0;
+
+ int64_t n_elements = 0;
+ size_t n_bytes = 0;
+
+ bool use_mmap = false;
+ bool check_tensors;
+
+ llama_files files;
+ llama_ftype ftype;
+ llama_fver fver;
+
+ llama_mmaps mappings;
+
+ // Holds information on a model weight
+ struct llama_tensor_weight {
+ uint16_t idx; // source file index
+ size_t offs; // tensor data offset in the original file
+
+ ggml_tensor * tensor;
+
+ llama_tensor_weight(const llama_file * file, uint16_t idx, const char * name, const struct gguf_context * gguf_ctx, ggml_tensor * tensor) : idx(idx), tensor(tensor) {
+ const int tensor_idx = gguf_find_tensor(gguf_ctx, name);
+ offs = gguf_get_data_offset(gguf_ctx) + gguf_get_tensor_offset(gguf_ctx, tensor_idx);
+
+ if (offs + ggml_nbytes(tensor) < offs || offs + ggml_nbytes(tensor) > file->size) {
+ throw std::runtime_error(format("tensor '%s' data is not within the file bounds, model is corrupted or incomplete", name));
+ }
+ }
+ };
+ std::vector<llama_tensor_weight> weights;
+
+ std::unordered_map<std::string, struct llama_model_kv_override> kv_overrides;
+
+ struct gguf_context * meta = NULL;
+ std::vector<ggml_context *> contexts;
+
+ std::string arch_name;
+ LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN);
+
+ llama_model_loader(const std::string & fname, bool use_mmap, bool check_tensors, const struct llama_model_kv_override * param_overrides_p) {
+ int trace = 0;
+ if (getenv("LLAMA_TRACE")) {
+ trace = atoi(getenv("LLAMA_TRACE"));
+ }
+
+ if (param_overrides_p != nullptr) {
+ for (const struct llama_model_kv_override * p = param_overrides_p; p->key[0] != 0; p++) {
+ kv_overrides.insert({std::string(p->key), *p});
+ }
+ }
+
+ struct ggml_context * ctx = NULL;
+ struct gguf_init_params params = {
+ /*.no_alloc = */ true,
+ /*.ctx = */ &ctx,
+ };
+
+ meta = gguf_init_from_file(fname.c_str(), params);
+ if (!meta) {
+ throw std::runtime_error(format("%s: failed to load model from %s\n", __func__, fname.c_str()));
+ }
+
+ get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
+ llm_kv = LLM_KV(llm_arch_from_string(arch_name));
+
+ files.emplace_back(new llama_file(fname.c_str(), "rb"));
+ contexts.emplace_back(ctx);
+
+ // Save tensors data offset of the main file.
+ // For subsidiary files, `meta` tensor data offset must not be used,
+ // so we build a unified tensors index for weights.
+ for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
+ weights.emplace_back(files.back().get(), 0, cur->name, meta, cur);
+ }
+ uint16_t n_split = 0;
+ get_key(llm_kv(LLM_KV_SPLIT_COUNT), n_split, false);
+
+ // Load additional GGML contexts
+ if (n_split > 1) {
+ uint16_t idx = 0;
+ get_key(llm_kv(LLM_KV_SPLIT_NO), idx);
+ if (idx != 0) {
+ throw std::runtime_error(format("illegal split file: %d, model must be loaded with the first split", idx));
+ }
+
+ char split_prefix[PATH_MAX] = {0};
+ if (!llama_split_prefix(split_prefix, sizeof(split_prefix), fname.c_str(), idx, n_split)) {
+ throw std::runtime_error(format("invalid split file: %s", fname.c_str()));
+ }
+
+ if (trace > 0) {
+ LLAMA_LOG_INFO("%s: loading additional %d GGUFs\n", __func__, n_split);
+ }
+
+ char split_path[PATH_MAX] = {0};
+ for (idx = 1; idx < n_split; idx++) {
+ llama_split_path(split_path, sizeof(split_path), split_prefix, idx, n_split);
+
+ struct gguf_init_params split_params = {
+ /*.no_alloc = */ true,
+ /*.ctx = */ &ctx,
+ };
+ struct gguf_context * ctx_gguf = gguf_init_from_file(split_path, split_params);
+ if (!ctx_gguf) {
+ throw std::runtime_error(format("%s: failed to load GGUF split from %s\n", __func__, split_path));
+ }
+
+ files.emplace_back(new llama_file(split_path, "rb"));
+ contexts.emplace_back(ctx);
+
+ // Save tensors data offset info of the shard.
+ for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
+ weights.emplace_back(files.back().get(), idx, cur->name, ctx_gguf, cur);
+ }
+
+ gguf_free(ctx_gguf);
+ }
+
+ get_key(llm_kv(LLM_KV_SPLIT_TENSORS_COUNT), n_tensors);
+
+ // sanity check
+ {
+ const int n_tensors_loaded = (int) weights.size();
+ if (n_tensors != n_tensors_loaded) {
+ throw std::runtime_error(format("corrupted model: %d tensors expected but %d found", n_tensors, n_tensors_loaded));
+ }
+ }
+
+ LLAMA_LOG_INFO("%s: additional %d GGUFs metadata loaded.\n", __func__, n_split - 1);
+ }
+
+ n_kv = gguf_get_n_kv(meta);
+ n_tensors = weights.size();
+
+ fver = (enum llama_fver) gguf_get_version(meta);
+
+ std::set<std::string> tensor_names;
+ for (auto & w : weights) {
+ n_elements += ggml_nelements(w.tensor);
+ n_bytes += ggml_nbytes(w.tensor);
+ // make sure there is no duplicated tensor names
+ const std::string name(w.tensor->name);
+ auto found = tensor_names.find(name);
+ if (found != tensor_names.end()) {
+ throw std::runtime_error(format("invalid model: tensor '%s' is duplicated", w.tensor->name));
+ }
+ tensor_names.insert(name);
+ }
+
+ LLAMA_LOG_INFO("%s: loaded meta data with %d key-value pairs and %d tensors from %s (version %s)\n",
+ __func__, n_kv, n_tensors, fname.c_str(), llama_file_version_name(fver));
+
+ // determine file type based on the number of tensors for each quantization and print meta data
+ // TODO: make optional
+ {
+ std::map<enum ggml_type, uint32_t> n_type;
+
+ uint32_t n_type_max = 0;
+ enum ggml_type type_max = GGML_TYPE_F32;
+
+ for (int i = 0; i < n_tensors; i++) {
+ const ggml_tensor * tensor = weights.at(i).tensor;
+ enum ggml_type type = tensor->type;
+
+ n_type[type]++;
+
+ if (n_type_max < n_type[type]) {
+ n_type_max = n_type[type];
+ type_max = type;
+ }
+
+ if (trace > 0) {
+ const uint16_t sid = weights.at(i).idx;
+ LLAMA_LOG_INFO("%s: - tensor %4d, split %2d: %32s %-8s [ %s ]\n", __func__, i, sid, ggml_get_name(tensor), ggml_type_name(type), llama_format_tensor_shape(tensor).c_str());
+ }
+ }
+
+ switch (type_max) {
+ case GGML_TYPE_F32: ftype = LLAMA_FTYPE_ALL_F32; break;
+ case GGML_TYPE_F16: ftype = LLAMA_FTYPE_MOSTLY_F16; break;
+ case GGML_TYPE_BF16: ftype = LLAMA_FTYPE_MOSTLY_BF16; break;
+ case GGML_TYPE_Q4_0: ftype = LLAMA_FTYPE_MOSTLY_Q4_0; break;
+ case GGML_TYPE_Q4_1: ftype = LLAMA_FTYPE_MOSTLY_Q4_1; break;
+ case GGML_TYPE_Q5_0: ftype = LLAMA_FTYPE_MOSTLY_Q5_0; break;
+ case GGML_TYPE_Q5_1: ftype = LLAMA_FTYPE_MOSTLY_Q5_1; break;
+ case GGML_TYPE_Q8_0: ftype = LLAMA_FTYPE_MOSTLY_Q8_0; break;
+ case GGML_TYPE_Q2_K: ftype = LLAMA_FTYPE_MOSTLY_Q2_K; break;
+ case GGML_TYPE_Q3_K: ftype = LLAMA_FTYPE_MOSTLY_Q3_K_M; break;
+ case GGML_TYPE_Q4_K: ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M; break;
+ case GGML_TYPE_Q5_K: ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M; break;
+ case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K; break;
+ case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break;
+ case GGML_TYPE_IQ2_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS; break;
+ case GGML_TYPE_IQ2_S: ftype = LLAMA_FTYPE_MOSTLY_IQ2_S; break;
+ case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break;
+ case GGML_TYPE_IQ1_S: ftype = LLAMA_FTYPE_MOSTLY_IQ1_S; break;
+ case GGML_TYPE_IQ1_M: ftype = LLAMA_FTYPE_MOSTLY_IQ1_M; break;
+ case GGML_TYPE_IQ1_BN: ftype = LLAMA_FTYPE_MOSTLY_IQ1_BN; break;
+ case GGML_TYPE_IQ2_BN: ftype = LLAMA_FTYPE_MOSTLY_IQ2_BN; break;
+ case GGML_TYPE_IQ4_NL: ftype = LLAMA_FTYPE_MOSTLY_IQ4_NL; break;
+ case GGML_TYPE_IQ4_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ4_XS; break;
+ case GGML_TYPE_IQ3_S: ftype = LLAMA_FTYPE_MOSTLY_IQ3_S; break;
+ case GGML_TYPE_Q4_0_4_4: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_4_4; break;
+ case GGML_TYPE_Q4_0_4_8: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_4_8; break;
+ case GGML_TYPE_Q4_0_8_8: ftype = LLAMA_FTYPE_MOSTLY_Q4_0_8_8; break;
+ default:
+ {
+ LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max));
+ ftype = LLAMA_FTYPE_ALL_F32;
+ } break;
+ }
+
+ // this is a way to mark that we have "guessed" the file type
+ ftype = (llama_ftype) (ftype | LLAMA_FTYPE_GUESSED);
+
+ {
+ const int kid = gguf_find_key(meta, "general.file_type"); // TODO: use LLM_KV
+ if (kid >= 0) {
+ ftype = (llama_ftype) gguf_get_val_u32(meta, kid);
+ }
+ }
+
+ LLAMA_LOG_INFO("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
+
+ for (int i = 0; i < n_kv; i++) {
+ const char * name = gguf_get_key(meta, i);
+ const enum gguf_type type = gguf_get_kv_type(meta, i);
+ const std::string type_name =
+ type == GGUF_TYPE_ARRAY
+ ? format("%s[%s,%d]", gguf_type_name(type), gguf_type_name(gguf_get_arr_type(meta, i)), gguf_get_arr_n(meta, i))
+ : gguf_type_name(type);
+
+ std::string value = gguf_kv_to_str(meta, i);
+ const size_t MAX_VALUE_LEN = 40;
+ if (value.size() > MAX_VALUE_LEN) {
+ value = format("%s...", value.substr(0, MAX_VALUE_LEN - 3).c_str());
+ }
+ replace_all(value, "\n", "\\n");
+
+ LLAMA_LOG_INFO("%s: - kv %3d: %42s %-16s = %s\n", __func__, i, name, type_name.c_str(), value.c_str());
+ }
+
+ // print type counts
+ for (auto & kv : n_type) {
+ if (kv.second == 0) {
+ continue;
+ }
+
+ LLAMA_LOG_INFO("%s: - type %4s: %4d tensors\n", __func__, ggml_type_name(kv.first), kv.second);
+ }
+ }
+
+ if (!llama_mmap::SUPPORTED) {
+ LLAMA_LOG_WARN("%s: mmap is not supported on this platform\n", __func__);
+ use_mmap = false;
+ }
+
+ this->use_mmap = use_mmap;
+ this->check_tensors = check_tensors;
+ }
+
+ ~llama_model_loader() {
+ if (meta) {
+ gguf_free(meta);
+ }
+ for (auto * ctx : contexts) {
+ ggml_free(ctx);
+ }
+ }
+
+ template<typename T>
+ typename std::enable_if<std::is_integral<T>::value, bool>::type
+ get_arr_n(const std::string & key, T & result, const bool required = true) {
+ const int kid = gguf_find_key(meta, key.c_str());
+
+ if (kid < 0) {
+ if (required) {
+ throw std::runtime_error(format("key not found in model: %s", key.c_str()));
+ }
+ return false;
+ }
+
+ struct GGUFMeta::ArrayInfo arr_info =
+ GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
+
+
+ result = arr_info.length;
+ return true;
+ }
+
+ template<typename T>
+ typename std::enable_if<std::is_integral<T>::value, bool>::type
+ get_arr_n(const enum llm_kv kid, T & result, const bool required = true) {
+ return get_arr_n(llm_kv(kid), result, required);
+ }
+
+ template<typename T>
+ bool get_arr(const std::string & key, std::vector<T> & result, const bool required = true) {
+ const int kid = gguf_find_key(meta, key.c_str());
+
+ if (kid < 0 || gguf_get_kv_type(meta, kid) != GGUF_TYPE_ARRAY) {
+ if (required) {
+ throw std::runtime_error(format("array key not found in model: %s", key.c_str()));
+ }
+ return false;
+ }
+
+ struct GGUFMeta::ArrayInfo arr_info =
+ GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
+
+ switch (arr_info.gt) {
+ case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
+ case GGUF_TYPE_INT32: GGML_ASSERT(
+ (std::is_same<T, int32_t>::value) ||
+ (std::is_same<T, uint32_t>::value)); break;
+ default:
+ throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str()));
+ }
+
+ result.resize(arr_info.length);
+ result.assign((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length);
+
+ return true;
+ }
+
+ template<typename T, size_t N_MAX>
+ bool get_arr(const std::string & key, std::array<T, N_MAX> & result, const bool required = true) {
+ const int kid = gguf_find_key(meta, key.c_str());
+
+ if (kid < 0 || gguf_get_kv_type(meta, kid) != GGUF_TYPE_ARRAY) {
+ if (required) {
+ throw std::runtime_error(format("array key not found in model: %s", key.c_str()));
+ }
+ return false;
+ }
+
+ struct GGUFMeta::ArrayInfo arr_info =
+ GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
+
+ switch (arr_info.gt) {
+ case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
+ case GGUF_TYPE_INT32: GGML_ASSERT(
+ (std::is_same<T, int32_t>::value) ||
+ (std::is_same<T, uint32_t>::value)); break;
+ default:
+ throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str()));
+ }
+
+ if (arr_info.length > N_MAX) {
+ throw std::runtime_error(format("array length %u for key %s exceeds max %u", (uint32_t) arr_info.length, key.c_str(), (uint32_t) N_MAX));
+ }
+
+ std::copy((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length, result.begin());
+
+ return true;
+ }
+
+ template<typename T>
+ bool get_arr(const enum llm_kv kid, T & result, const bool required = true) {
+ return get_arr(llm_kv(kid), result, required);
+ }
+
+ template<typename T>
+ bool get_key(const std::string & key, T & result, const bool required = true) {
+ auto it = kv_overrides.find(key);
+
+ const struct llama_model_kv_override * override =
+ it != kv_overrides.end() ? &it->second : nullptr;
+
+ const bool found = GGUFMeta::GKV<T>::set(meta, key, result, override);
+
+ if (required && !found) {
+ throw std::runtime_error(format("key not found in model: %s", key.c_str()));
+ }
+
+ return found;
+ }
+
+ template<typename T>
+ bool get_key(const enum llm_kv kid, T & result, const bool required = true) {
+ return get_key(llm_kv(kid), result, required);
+ }
+
+ // get array of n <= N_MAX elements, or a single element repeated n times
+ template<typename T, size_t N_MAX>
+ bool get_key_or_arr(const std::string & key, std::array<T, N_MAX> & result, uint32_t n, const bool required = true) {
+ const int kid = gguf_find_key(meta, key.c_str());
+
+ if (kid < 0) {
+ if (required) {
+ throw std::runtime_error(format("key not found in model: %s", key.c_str()));
+ }
+ return false;
+ }
+
+ if (n > N_MAX) {
+ throw std::runtime_error(format("n > N_MAX: %u > %u for key %s", (uint32_t) n, (uint32_t) N_MAX, key.c_str()));
+ }
+
+ if (gguf_get_kv_type(meta, kid) == GGUF_TYPE_ARRAY) {
+ struct GGUFMeta::ArrayInfo arr_info =
+ GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
+
+ if (n != arr_info.length) {
+ throw std::runtime_error(format("key %s has wrong array length; expected %u, got %u", key.c_str(), n, (uint32_t) arr_info.length));
+ }
+
+ return get_arr(key, result, required);
+ } else {
+ T value;
+
+ bool ok = get_key(key, value, required);
+ if (!ok) {
+ return false;
+ }
+
+ for (uint32_t i = 0; i < n; i++) {
+ result[i] = value;
+ }
+
+ return true;
+ }
+ }
+
+ template<typename T>
+ bool get_key_or_arr(const enum llm_kv kid, T & result, uint32_t n, const bool required = true) {
+ return get_key_or_arr(llm_kv(kid), result, n, required);
+ }
+
+ std::string get_arch_name() const {
+ return arch_name;
+ }
+
+ enum llm_arch get_arch() const {
+ return llm_kv.arch;
+ }
+
+ const char * get_tensor_name(int i) const {
+ return weights.at(i).tensor->name;
+ }
+
+ const llama_tensor_weight * get_weight(const char * name) const {
+ for (const auto & weight : weights) {
+ if (strcmp(name, weight.tensor->name) == 0) {
+ return &weight;
+ }
+ }
+ return nullptr;
+ }
+
+ const llama_tensor_weight * get_weight(int i) const {
+ return get_weight(get_tensor_name(i));
+ }
+
+ const llama_tensor_weight & require_weight(const char * name) const {
+ const llama_tensor_weight * weight = get_weight(name);
+ if (!weight) {
+ throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name));
+ }
+ return *weight;
+ }
+
+ struct ggml_tensor * get_tensor_meta(const char * name) const {
+ const auto * weight = get_weight(name);
+ if (!weight) {
+ return nullptr;
+ }
+ return weight->tensor;
+ }
+
+ struct ggml_tensor * require_tensor_meta(const char * name) const {
+ struct ggml_tensor * tensor = get_tensor_meta(name);
+ if (!tensor) {
+ throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name));
+ }
+ return tensor;
+ }
+
+ struct ggml_tensor * get_tensor_meta(int i) const {
+ return get_tensor_meta(get_tensor_name(i));
+ }
+
+ struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur, bool duplicated) {
+ struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur);
+ ggml_set_name(tensor, ggml_get_name(cur));
+
+ if (duplicated) {
+ size_data += ggml_nbytes(cur);
+ } else {
+ n_created++;
+ }
+
+ return tensor;
+ }
+
+ const struct ggml_tensor * check_tensor_dims(const std::string & name, const std::vector<int64_t> & ne, bool required) const {
+ const struct ggml_tensor * cur = get_tensor_meta(name.c_str());
+
+ if (cur == NULL) {
+ if (!required) {
+ return NULL;
+ }
+ throw std::runtime_error(format("%s: tensor '%s' not found", __func__, name.c_str()));
+ }
+
+ {
+ bool is_ok = true;
+ for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
+ if ((i < ne.size() && ne[i] != cur->ne[i]) || (i >= ne.size() && cur->ne[i] != 1)) {
+ is_ok = false;
+ break;
+ }
+ }
+ if (!is_ok) {
+ throw std::runtime_error(
+ format("%s: tensor '%s' has wrong shape; expected %s, got %s",
+ __func__, name.c_str(),
+ llama_format_tensor_shape(ne).c_str(),
+ llama_format_tensor_shape(cur).c_str()));
+ }
+ }
+
+ return cur;
+ }
+
+ static const int TENSOR_NOT_REQUIRED = 1;
+ static const int TENSOR_DUPLICATED = 2;
+
+ struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector<int64_t> & ne, int flags = 0) {
+ const struct ggml_tensor * cur = check_tensor_dims(name, ne, !(flags & TENSOR_NOT_REQUIRED));
+
+ if (cur == NULL) {
+ return NULL;
+ }
+
+ return create_tensor_for(ctx, cur, flags & TENSOR_DUPLICATED);
+ }
+
+ struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::vector<int64_t> & ne, size_t offset, bool required = true) {
+ const struct ggml_tensor * cur = check_tensor_dims(name, ne, required);
+
+ if (cur == NULL) {
+ return NULL;
+ }
+
+ if (cur->type != base->type) {
+ throw std::runtime_error(format("%s: tensor '%s' has wrong type; expected %s, got %s", __func__, name.c_str(), ggml_type_name(base->type), ggml_type_name(cur->type)));
+ }
+
+ std::array<int64_t, GGML_MAX_DIMS> dims;
+ for (size_t i = 0; i < GGML_MAX_DIMS; ++i) {
+ dims[i] = i < ne.size() ? ne[i] : 1;
+ }
+
+ struct ggml_tensor * tensor = ggml_view_4d(ctx, base,
+ dims[0], dims[1], dims[2], dims[3],
+ cur->nb[1], cur->nb[2], cur->nb[3],
+ offset);
+
+ ggml_set_name(tensor, name.c_str());
+
+ n_created++;
+
+ return tensor;
+ }
+
+ void done_getting_tensors() const {
+ if (n_created != n_tensors) {
+ throw std::runtime_error(format("%s: wrong number of tensors; expected %d, got %d", __func__, n_tensors, n_created));
+ }
+ }
+
+ void init_mappings(bool prefetch = true, llama_mlocks * mlock_mmaps = nullptr) {
+ if (use_mmap) {
+ mappings.reserve(files.size());
+ mmaps_used.reserve(files.size());
+ for (const auto & file : files) {
+ std::unique_ptr<llama_mmap> mapping(new llama_mmap(file.get(), prefetch ? -1 : 0, ggml_is_numa()));
+ mmaps_used.emplace_back(mapping->size, 0);
+ if (mlock_mmaps) {
+ std::unique_ptr<llama_mlock> mlock_mmap(new llama_mlock());
+ mlock_mmap->init(mapping->addr);
+ mlock_mmaps->emplace_back(std::move(mlock_mmap));
+ }
+ mappings.emplace_back(std::move(mapping));
+ }
+ }
+
+ // compute the total size of all tensors for progress reporting
+ for (auto & w : weights) {
+ size_data += ggml_nbytes(w.tensor);
+ }
+ }
+
+ void get_mapping_range(size_t * first, size_t * last, void ** addr, int idx, ggml_context * ctx) const {
+ GGML_ASSERT(!mappings.empty());
+ const auto & mapping = mappings.at(idx);
+
+ *first = mapping->size;
+ *last = 0;
+ *addr = mapping->addr;
+ for (ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor; tensor = ggml_get_next_tensor(ctx, tensor)) {
+ try {
+ const auto * weight = get_weight(ggml_get_name(tensor));
+ if (!weight) {
+ continue;
+ }
+ if (weight->idx != idx) {
+ continue;
+ }
+ *first = std::min(*first, weight->offs);
+ *last = std::max(*last, weight->offs + ggml_nbytes(tensor));
+ } catch(...) {
+ // the tensor is not in the model
+ }
+ }
+ }
+
+ // for backwards compatibility, does not support ggml-backend
+ void load_data_for(struct ggml_tensor * cur) const {
+ const auto & w = require_weight(ggml_get_name(cur));
+
+ if (use_mmap) {
+ const auto & mapping = mappings.at(w.idx);
+ if (cur->data == nullptr) {
+ cur->data = (uint8_t *)mapping->addr + w.offs;
+ } else {
+ memcpy(cur->data, (uint8_t *)mapping->addr + w.offs, ggml_nbytes(cur));
+ }
+ } else {
+ GGML_ASSERT(cur->data != nullptr);
+ GGML_ASSERT(w.idx < files.size());
+ const auto & file = files.at(w.idx);
+ file->seek(w.offs, SEEK_SET);
+ file->read_raw(cur->data, ggml_nbytes(cur));
+ }
+
+ if (check_tensors && !ggml_validate_row_data(cur->type, cur->data, ggml_nbytes(cur))) {
+ throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
+ }
+ }
+
+ size_t size_done = 0;
+ size_t size_data = 0;
+ std::vector<std::pair<size_t, size_t>> mmaps_used;
+
+ // Returns false if cancelled by progress_callback
+ bool load_all_data(
+ struct ggml_context * ctx,
+ llama_buf_map & bufs_mmap,
+ llama_mlocks * lmlocks,
+ llama_progress_callback progress_callback,
+ void * progress_callback_user_data) {
+ GGML_ASSERT(size_data != 0 && "call init_mappings() first");
+
+ std::vector<no_init<uint8_t>> read_buf;
+ std::vector<std::future<std::pair<ggml_tensor *, bool>>> validation_result;
+
+#if defined(GGML_USE_CUDA)
+ // 4 staging buffers for async uploads, each sized 1MB seems to be a good default for single NVMe drives.
+ // NVMe raid configurations might require more / larger buffers.
+ constexpr size_t n_buffers = 4;
+ constexpr size_t buffer_size = 1 * 1024 * 1024; // 1MB
+
+ std::vector<ggml_backend_buffer_t> host_buffers;
+ std::vector<void*> host_ptrs;
+ std::vector<ggml_backend_event_t> events;
+ size_t buffer_idx = 0; // buffer to use for async loads
+
+ ggml_backend_t cuda_backend = nullptr;
+ if (!use_mmap && !check_tensors) {
+ // When not using mmaped io use async uploads from pinned memory to GPU memory.
+ // First determine if the CUDA backend is active, and if so, determine the device ID.
+ ggml_backend_buffer_t buf = bufs_mmap.count(0) ? bufs_mmap.at(0) : nullptr;
+ if (buf) {
+ ggml_backend_buffer_type_t buffer_type = ggml_backend_buffer_get_type(buf);
+ for (int i = 0; i < ggml_backend_cuda_get_device_count(); ++i) {
+ auto * cuda_buffer_type = ggml_backend_cuda_buffer_type(i);
+ if (buffer_type == cuda_buffer_type) {
+ cuda_backend = ggml_backend_cuda_init(i);
+ break;
+ }
+ }
+ }
+
+ // If the cuda backend is active create pinned memory buffers and events for synchronisation.
+ if (cuda_backend) {
+ for (size_t idx = 0; idx < n_buffers; ++idx) {
+ host_buffers.emplace_back(ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), buffer_size));
+ host_ptrs.emplace_back(ggml_backend_buffer_get_base(host_buffers[idx]));
+ events.emplace_back(ggml_backend_event_new(cuda_backend));
+ }
+ }
+ }
+#endif
+
+ for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
+ const auto * weight = get_weight(ggml_get_name(cur));
+ if (weight == nullptr) {
+ // this can happen with split experts models
+ continue;
+ }
+
+ if (progress_callback) {
+ if (!progress_callback((float) size_done / size_data, progress_callback_user_data)) {
+ return false;
+ }
+ }
+
+ size_t n_size = ggml_nbytes(cur);
+
+ if (use_mmap) {
+ const auto & mapping = mappings.at(weight->idx);
+ ggml_backend_buffer_t buf_mmap = nullptr;
+ if (bufs_mmap.count(weight->idx)) {
+ buf_mmap = bufs_mmap.at(weight->idx);
+ }
+ uint8_t * data = (uint8_t *) mapping->addr + weight->offs;
+
+ if (check_tensors) {
+ validation_result.emplace_back(std::async(std::launch::async, [cur, data, n_size] {
+ return std::make_pair(cur, ggml_validate_row_data(cur->type, data, n_size));
+ }));
+ }
+
+ GGML_ASSERT(buf_mmap || cur->data); // either we have a buffer to allocate the tensor in, or it is already allocated
+ if (buf_mmap && cur->data == nullptr) {
+ ggml_backend_tensor_alloc(buf_mmap, cur, data);
+ if (lmlocks) {
+ const auto & lmlock = lmlocks->at(weight->idx);
+ lmlock->grow_to(weight->offs + n_size);
+ }
+
+ auto & mmap_used = mmaps_used[weight->idx];
+ mmap_used.first = std::min(mmap_used.first, weight->offs);
+ mmap_used.second = std::max(mmap_used.second, weight->offs + n_size);
+ } else {
+ ggml_backend_tensor_set(cur, data, 0, n_size);
+ }
+ } else {
+ GGML_ASSERT(weight->idx < files.size());
+ const auto & file = files.at(weight->idx);
+ if (ggml_backend_buffer_is_host(cur->buffer)) {
+ file->seek(weight->offs, SEEK_SET);
+ file->read_raw(cur->data, n_size);
+ if (check_tensors) {
+ validation_result.emplace_back(std::async(std::launch::async, [cur, n_size] {
+ return std::make_pair(cur, ggml_validate_row_data(cur->type, cur->data, n_size));
+ }));
+ }
+ } else {
+#if defined(GGML_USE_CUDA)
+ // If cuda_backend is valid load the tensor in chunks to pinned memory and upload the buffers asynchronously to the GPU.
+ if (cuda_backend) {
+ file->seek(weight->offs, SEEK_SET);
+
+ size_t bytes_read = 0;
+
+ while (bytes_read < n_size) {
+ size_t read_iteration = std::min<size_t>(buffer_size, n_size - bytes_read);
+
+ ggml_backend_event_synchronize(events[buffer_idx]);
+ file->read_raw(host_ptrs[buffer_idx], read_iteration);
+ ggml_backend_tensor_set_async(cuda_backend, cur, host_ptrs[buffer_idx], bytes_read, read_iteration);
+ ggml_backend_event_record(events[buffer_idx]);
+
+ bytes_read += read_iteration;
+ ++buffer_idx;
+ buffer_idx %= n_buffers;
+ }
+ }
+ else
+#endif
+ {
+ read_buf.resize(n_size);
+ file->seek(weight->offs, SEEK_SET);
+ file->read_raw(read_buf.data(), n_size);
+ ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size);
+ if (check_tensors && !ggml_validate_row_data(cur->type, read_buf.data(), n_size)) {
+ throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));
+ }
+ }
+ }
+ }
+
+ size_done += n_size;
+ }
+
+#if defined(GGML_USE_CUDA)
+ // free temporary resources used for async cuda uploads
+ if (cuda_backend) {
+ for (size_t idx = 0; idx < n_buffers;++idx) {
+ ggml_backend_event_synchronize(events[idx]);
+ ggml_backend_event_free(events[idx]);
+ ggml_backend_buffer_free(host_buffers[idx]);
+ }
+ ggml_backend_free(cuda_backend);
+ }
+#endif
+
+ // check validation results
+ bool validation_failed = false;
+ for (auto & future : validation_result) {
+ auto result = future.get();
+ if (!result.second) {
+ LLAMA_LOG_ERROR("%s: tensor '%s' has invalid data\n", __func__, ggml_get_name(result.first));
+ validation_failed = true;
+ }
+ }
+ if (validation_failed) {
+ throw std::runtime_error("found tensors with invalid data");
+ }
+
+ // check if this is the last call and do final cleanup
+ if (size_done >= size_data) {
+ // unmap offloaded tensors and metadata
+ if (use_mmap) {
+ for (uint32_t idx = 0; idx < mappings.size(); idx++) {
+ const auto & mmap_used = mmaps_used.at(idx);
+ auto & mapping = mappings.at(idx);
+ mapping->unmap_fragment(0, mmap_used.first);
+ if (mmap_used.second != 0) {
+ mapping->unmap_fragment(mmap_used.second, mapping->size);
+ }
+ }
+ }
+ if (progress_callback) {
+ // Even though the model is done loading, we still honor
+ // cancellation since we need to free allocations.
+ return progress_callback(1.0f, progress_callback_user_data);
+ }
+ }
+
+ return true;
+ }
+};
+
+template<>
+bool llama_model_loader::get_key(const enum llm_kv kid, enum llama_pooling_type & result, const bool required) {
+ uint32_t tmp;
+ const bool found = get_key(kid, tmp, required);
+ if (found) {
+ result = (enum llama_pooling_type) tmp;
+ } else {
+ result = LLAMA_POOLING_TYPE_UNSPECIFIED;
+ }
+ return found;
+}
+
+
+//
+// load LLaMA models
+//
+
+static const char * llama_model_arch_name(llm_arch arch) {
+ auto it = LLM_ARCH_NAMES.find(arch);
+ if (it == LLM_ARCH_NAMES.end()) {
+ return "unknown";
+ }
+ return it->second;
+}
+
+static std::string llama_model_ftype_name(llama_ftype ftype) {
+ if (ftype & LLAMA_FTYPE_GUESSED) {
+ return llama_model_ftype_name((enum llama_ftype) (ftype & ~LLAMA_FTYPE_GUESSED)) + " (guessed)";
+ }
+
+ switch (ftype) {
+ case LLAMA_FTYPE_ALL_F32: return "all F32";
+ case LLAMA_FTYPE_MOSTLY_F16: return "F16";
+ case LLAMA_FTYPE_MOSTLY_BF16: return "BF16";
+ case LLAMA_FTYPE_MOSTLY_Q4_0: return "Q4_0";
+ case LLAMA_FTYPE_MOSTLY_Q4_1: return "Q4_1";
+ case LLAMA_FTYPE_MOSTLY_Q5_0: return "Q5_0";
+ case LLAMA_FTYPE_MOSTLY_Q5_1: return "Q5_1";
+ case LLAMA_FTYPE_MOSTLY_Q8_0: return "Q8_0";
+ case LLAMA_FTYPE_MOSTLY_Q2_K: return "Q2_K - Medium";
+ case LLAMA_FTYPE_MOSTLY_Q2_K_S: return "Q2_K - Small";
+ case LLAMA_FTYPE_MOSTLY_Q3_K_S: return "Q3_K - Small";
+ case LLAMA_FTYPE_MOSTLY_Q3_K_M: return "Q3_K - Medium";
+ case LLAMA_FTYPE_MOSTLY_Q3_K_L: return "Q3_K - Large";
+ case LLAMA_FTYPE_MOSTLY_Q4_K_S: return "Q4_K - Small";
+ case LLAMA_FTYPE_MOSTLY_Q4_K_M: return "Q4_K - Medium";
+ case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "Q5_K - Small";
+ case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "Q5_K - Medium";
+ case LLAMA_FTYPE_MOSTLY_Q6_K: return "Q6_K";
+ case LLAMA_FTYPE_MOSTLY_IQ2_XXS: return "IQ2_XXS - 2.0625 bpw";
+ case LLAMA_FTYPE_MOSTLY_IQ2_XS: return "IQ2_XS - 2.3125 bpw";
+ case LLAMA_FTYPE_MOSTLY_IQ2_S: return "IQ2_S - 2.5 bpw";
+ case LLAMA_FTYPE_MOSTLY_IQ2_M: return "IQ2_M - 2.7 bpw";
+ case LLAMA_FTYPE_MOSTLY_IQ3_XS: return "IQ3_XS - 3.3 bpw";
+ case LLAMA_FTYPE_MOSTLY_IQ3_XXS: return "IQ3_XXS - 3.0625 bpw";
+ case LLAMA_FTYPE_MOSTLY_IQ1_S: return "IQ1_S - 1.5625 bpw";
+ case LLAMA_FTYPE_MOSTLY_IQ1_M: return "IQ1_M - 1.75 bpw";
+ case LLAMA_FTYPE_MOSTLY_IQ4_NL: return "IQ4_NL - 4.5 bpw";
+ case LLAMA_FTYPE_MOSTLY_IQ4_XS: return "IQ4_XS - 4.25 bpw";
+ case LLAMA_FTYPE_MOSTLY_IQ3_S: return "IQ3_S - 3.4375 bpw";
+ case LLAMA_FTYPE_MOSTLY_IQ3_M: return "IQ3_S mix - 3.66 bpw";
+ case LLAMA_FTYPE_MOSTLY_Q4_0_4_4: return "Q4_0_4_4";
+ case LLAMA_FTYPE_MOSTLY_Q4_0_4_8: return "Q4_0_4_8";
+ case LLAMA_FTYPE_MOSTLY_Q4_0_8_8: return "Q4_0_8_8";
+ case LLAMA_FTYPE_MOSTLY_IQ1_BN: return "IQ1_BN - 1.625 bpw Bitnet";
+ case LLAMA_FTYPE_MOSTLY_IQ2_BN: return "IQ2_BN - 2.00 bpw Bitnet";
+
+ default: return "unknown, may not work";
+ }
+}
+
+static const char * llama_model_type_name(e_model type) {
+ switch (type) {
+ case MODEL_14M: return "14M";
+ case MODEL_17M: return "17M";
+ case MODEL_22M: return "22M";
+ case MODEL_33M: return "33M";
+ case MODEL_60M: return "60M";
+ case MODEL_70M: return "70M";
+ case MODEL_80M: return "80M";
+ case MODEL_109M: return "109M";
+ case MODEL_137M: return "137M";
+ case MODEL_160M: return "160M";
+ case MODEL_220M: return "220M";
+ case MODEL_250M: return "250M";
+ case MODEL_270M: return "270M";
+ case MODEL_335M: return "335M";
+ case MODEL_410M: return "410M";
+ case MODEL_450M: return "450M";
+ case MODEL_770M: return "770M";
+ case MODEL_780M: return "780M";
+ case MODEL_0_5B: return "0.5B";
+ case MODEL_1B: return "1B";
+ case MODEL_1_3B: return "1.3B";
+ case MODEL_1_4B: return "1.4B";
+ case MODEL_2B: return "2B";
+ case MODEL_2_8B: return "2.8B";
+ case MODEL_3B: return "3B";
+ case MODEL_4B: return "4B";
+ case MODEL_6B: return "6B";
+ case MODEL_6_9B: return "6.9B";
+ case MODEL_7B: return "7B";
+ case MODEL_8B: return "8B";
+ case MODEL_9B: return "9B";
+ case MODEL_11B: return "11B";
+ case MODEL_12B: return "12B";
+ case MODEL_13B: return "13B";
+ case MODEL_14B: return "14B";
+ case MODEL_15B: return "15B";
+ case MODEL_16B: return "16B";
+ case MODEL_20B: return "20B";
+ case MODEL_30B: return "30B";
+ case MODEL_34B: return "34B";
+ case MODEL_35B: return "35B";
+ case MODEL_40B: return "40B";
+ case MODEL_65B: return "65B";
+ case MODEL_70B: return "70B";
+ case MODEL_236B: return "236B";
+ case MODEL_314B: return "314B";
+ case MODEL_SMALL: return "0.1B";
+ case MODEL_MEDIUM: return "0.4B";
+ case MODEL_LARGE: return "0.8B";
+ case MODEL_XL: return "1.5B";
+ case MODEL_A2_7B: return "A2.7B";
+ case MODEL_8x7B: return "8x7B";
+ case MODEL_8x22B: return "8x22B";
+ case MODEL_16x12B: return "16x12B";
+ case MODEL_10B_128x3_66B: return "10B+128x3.66B";
+ case MODEL_57B_A14B: return "57B.A14B";
+ case MODEL_27B: return "27B";
+ default: return "?B";
+ }
+}
+
+static const char * llama_model_vocab_type_name(enum llama_vocab_type type){
+ switch (type) {
+ case LLAMA_VOCAB_TYPE_NONE: return "no vocab";
+ case LLAMA_VOCAB_TYPE_SPM: return "SPM";
+ case LLAMA_VOCAB_TYPE_BPE: return "BPE";
+ case LLAMA_VOCAB_TYPE_WPM: return "WPM";
+ case LLAMA_VOCAB_TYPE_UGM: return "UGM";
+ default: return "unknown";
+ }
+}
+
+static void llm_load_arch(llama_model_loader & ml, llama_model & model) {
+ model.arch = ml.get_arch();
+ if (model.arch == LLM_ARCH_UNKNOWN) {
+ throw std::runtime_error("unknown model architecture: '" + ml.get_arch_name() + "'");
+ }
+}
+
+static void llm_load_hparams(
+ llama_model_loader & ml,
+ llama_model & model) {
+ auto & hparams = model.hparams;
+ const gguf_context * ctx = ml.meta;
+
+ // get metadata as string
+ for (int i = 0; i < gguf_get_n_kv(ctx); i++) {
+ enum gguf_type type = gguf_get_kv_type(ctx, i);
+ if (type == GGUF_TYPE_ARRAY) {
+ continue;
+ }
+ const char * name = gguf_get_key(ctx, i);
+ const std::string value = gguf_kv_to_str(ctx, i);
+ model.gguf_kv.emplace(name, value);
+ }
+
+ // get general kv
+ ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
+
+ // get hparams kv
+ ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab);
+
+ // everything past this point is not vocab-related
+ if (hparams.vocab_only) {
+ return;
+ }
+
+ ml.get_key(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train);
+ ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd);
+ ml.get_key(LLM_KV_BLOCK_COUNT, hparams.n_layer);
+ ml.get_key(LLM_KV_EXPERT_COUNT, hparams.n_expert, false);
+ ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
+
+ GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
+ GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
+ if (hparams.n_expert > 0) {
+ GGML_ASSERT(hparams.n_expert_used > 0);
+ } else {
+ GGML_ASSERT(hparams.n_expert_used == 0);
+ }
+
+ // zero-out the per-layer hparams
+ std::fill(hparams.n_head_arr.begin(), hparams.n_head_arr.end(), 0);
+ std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0);
+ std::fill(hparams.n_ff_arr.begin(), hparams.n_ff_arr.end(), 0);
+
+ ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff_arr, hparams.n_layer);
+ ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer);
+
+ // n_head_kv is optional, default to n_head
+ hparams.n_head_kv_arr = hparams.n_head_arr;
+
+ ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv_arr, hparams.n_layer, false);
+
+ bool rope_finetuned = false;
+ ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
+ hparams.rope_finetuned = rope_finetuned;
+
+ hparams.n_ctx_orig_yarn = hparams.n_ctx_train;
+ ml.get_key(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_ctx_orig_yarn, false);
+
+ // rope_freq_base (optional)
+ hparams.rope_freq_base_train = 10000.0f;
+ ml.get_key(LLM_KV_ROPE_FREQ_BASE, hparams.rope_freq_base_train, false);
+
+ std::string rope_scaling("linear");
+ ml.get_key(LLM_KV_ROPE_SCALING_TYPE, rope_scaling, false);
+ hparams.rope_scaling_type_train = llama_rope_scaling_type_from_string(rope_scaling);
+ GGML_ASSERT(hparams.rope_scaling_type_train != LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED);
+
+ // rope_freq_scale (inverse of the kv) is optional
+ float ropescale = 0.0f;
+ if (!ml.get_key(LLM_KV_ROPE_SCALING_FACTOR, ropescale, false)) {
+ // try the old key name
+ ml.get_key(LLM_KV_ROPE_SCALE_LINEAR, ropescale, false);
+ }
+ hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale;
+
+ ml.get_key(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor, false);
+
+ // non-transformer models do not have attention heads
+ if (hparams.n_head() > 0) {
+ // gpt-neox n_rot = rotary_pct * (n_embd / n_head)
+ // gpt-j n_rot = rotary_dim
+
+ hparams.n_embd_head_k = hparams.n_embd / hparams.n_head();
+ ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);
+
+ hparams.n_embd_head_v = hparams.n_embd / hparams.n_head();
+ ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
+
+ // sanity check for n_rot (optional)
+ hparams.n_rot = hparams.n_embd_head_k;
+
+ ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
+
+ if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) {
+ if (hparams.n_rot != hparams.n_embd_head_k) {
+ throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd_head_k));
+ }
+ }
+ } else {
+ hparams.n_rot = 0;
+ hparams.n_embd_head_k = 0;
+ hparams.n_embd_head_v = 0;
+ }
+
+ // arch-specific KVs
+ switch (model.arch) {
+ case LLM_ARCH_LLAMA:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ if (hparams.n_expert == 8) {
+ switch (hparams.n_layer) {
+ case 32: model.type = e_model::MODEL_8x7B; break;
+ case 56: model.type = e_model::MODEL_8x22B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } else {
+ switch (hparams.n_layer) {
+ case 22: model.type = e_model::MODEL_1B; break;
+ case 26: model.type = e_model::MODEL_3B; break;
+ // granite uses a vocab with len 49152
+ case 32: model.type = hparams.n_vocab == 49152 ? e_model::MODEL_3B : (hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B); break;
+ case 36: model.type = e_model::MODEL_8B; break; // granite
+ case 40: model.type = e_model::MODEL_13B; break;
+ case 48: model.type = e_model::MODEL_34B; break;
+ case 60: model.type = e_model::MODEL_30B; break;
+ case 80: model.type = hparams.n_head() == hparams.n_head_kv() ? e_model::MODEL_65B : e_model::MODEL_70B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ }
+ } break;
+ case LLM_ARCH_MINICPM:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ switch (hparams.n_layer) {
+ case 40: model.type = e_model::MODEL_2B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_GROK:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ switch (hparams.n_layer) {
+ case 64: model.type = e_model::MODEL_314B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_FALCON:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+
+ switch (hparams.n_layer) {
+ case 32: model.type = e_model::MODEL_7B; break;
+ case 60: model.type = e_model::MODEL_40B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_BAICHUAN:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ switch (hparams.n_layer) {
+ case 32: model.type = e_model::MODEL_7B; break;
+ case 40: model.type = e_model::MODEL_13B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+
+ if (model.type == e_model::MODEL_13B) {
+ // TODO: become GGUF KV parameter
+ hparams.f_max_alibi_bias = 8.0f;
+ }
+ } break;
+ case LLM_ARCH_STARCODER:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ switch (hparams.n_layer) {
+ case 24: model.type = e_model::MODEL_1B; break;
+ case 36: model.type = e_model::MODEL_3B; break;
+ case 42: model.type = e_model::MODEL_7B; break;
+ case 40: model.type = e_model::MODEL_15B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_REFACT:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ switch (hparams.n_layer) {
+ case 32: model.type = e_model::MODEL_1B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+
+ // TODO: become GGUF KV parameter
+ hparams.f_max_alibi_bias = 8.0f;
+ } break;
+ case LLM_ARCH_BERT:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
+ ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
+ ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
+
+ switch (hparams.n_layer) {
+ case 3:
+ model.type = e_model::MODEL_17M; break; // bge-micro
+ case 6:
+ model.type = e_model::MODEL_22M; break; // MiniLM-L6
+ case 12:
+ switch (hparams.n_embd) {
+ case 384: model.type = e_model::MODEL_33M; break; // MiniLM-L12, bge-small
+ case 768: model.type = e_model::MODEL_109M; break; // bge-base
+ } break;
+ case 24:
+ model.type = e_model::MODEL_335M; break; // bge-large
+ }
+ } break;
+ case LLM_ARCH_JINA_BERT_V2:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
+ ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
+ ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
+ hparams.f_max_alibi_bias = 8.0f;
+
+ switch (hparams.n_layer) {
+ case 4: model.type = e_model::MODEL_33M; break; // jina-embeddings-small
+ case 12: model.type = e_model::MODEL_137M; break; // jina-embeddings-base
+ }
+ } break;
+ case LLM_ARCH_NOMIC_BERT:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
+ ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
+ ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
+
+ if (hparams.n_layer == 12 && hparams.n_embd == 768) {
+ model.type = e_model::MODEL_137M;
+ }
+ } break;
+ case LLM_ARCH_BLOOM:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+
+ switch (hparams.n_layer) {
+ case 24: model.type = e_model::MODEL_1B; break;
+ case 30:
+ switch (hparams.n_embd) {
+ case 2560: model.type = e_model::MODEL_3B; break;
+ case 4096: model.type = e_model::MODEL_7B; break;
+ } break;
+ }
+
+ // TODO: become GGUF KV parameter
+ hparams.f_max_alibi_bias = 8.0f;
+ } break;
+ case LLM_ARCH_MPT:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv, false);
+ ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
+
+ switch (hparams.n_layer) {
+ case 32: model.type = e_model::MODEL_7B; break;
+ case 48: model.type = e_model::MODEL_30B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_STABLELM:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+
+ switch (hparams.n_layer) {
+ case 24: model.type = e_model::MODEL_1B; break;
+ case 32: model.type = e_model::MODEL_3B; break;
+ case 40: model.type = e_model::MODEL_12B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_QWEN:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ switch (hparams.n_layer) {
+ case 32: model.type = e_model::MODEL_7B; break;
+ case 40: model.type = e_model::MODEL_13B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_QWEN2:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ switch (hparams.n_layer) {
+ case 24: model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_5B : e_model::MODEL_1B; break;
+ case 32: model.type = e_model::MODEL_7B; break;
+ case 40: model.type = hparams.n_head() == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break;
+ case 80: model.type = e_model::MODEL_70B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_QWEN2MOE:
+ {
+ ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false);
+ ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false);
+
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ switch (hparams.n_layer) {
+ case 24: model.type = e_model::MODEL_A2_7B; break;
+ case 28: model.type = e_model::MODEL_57B_A14B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_PHI2:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+
+ switch (hparams.n_layer) {
+ case 24: model.type = e_model::MODEL_1B; break;
+ case 32: model.type = e_model::MODEL_3B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_PHI3:
+ {
+ ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa);
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ switch (hparams.n_layer) {
+ case 24: model.type = e_model::MODEL_1B; break;
+ case 32: model.type = e_model::MODEL_3B; break;
+ case 40: model.type = e_model::MODEL_14B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_PLAMO:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ switch (hparams.n_layer) {
+ case 40: model.type = e_model::MODEL_13B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_GPT2:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ switch (hparams.n_layer) {
+ case 12: model.type = e_model::MODEL_SMALL; break;
+ case 24: model.type = e_model::MODEL_MEDIUM; break;
+ case 36: model.type = e_model::MODEL_LARGE; break;
+ case 48: model.type = e_model::MODEL_XL; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_CODESHELL:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ switch (hparams.n_layer) {
+ case 42: model.type = e_model::MODEL_7B; break;
+ 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;
+ case LLM_ARCH_INTERNLM2:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ switch (hparams.n_layer) {
+ case 32: model.type = e_model::MODEL_7B; break;
+ case 48: model.type = e_model::MODEL_20B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_GEMMA:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ switch (hparams.n_layer) {
+ case 18: model.type = e_model::MODEL_2B; break;
+ case 28: model.type = e_model::MODEL_7B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_GEMMA2:
+ {
+ hparams.n_swa = 4096; // default value of gemma 2
+ ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING, hparams.f_attn_logit_softcapping, false);
+ ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
+ hparams.attn_soft_cap = true;
+
+ switch (hparams.n_layer) {
+ case 42: model.type = e_model::MODEL_9B; break;
+ case 46: model.type = e_model::MODEL_27B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_STARCODER2:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ switch (hparams.n_layer) {
+ case 30: model.type = e_model::MODEL_3B; break;
+ case 32: model.type = e_model::MODEL_7B; break;
+ case 40: model.type = e_model::MODEL_15B; break;
+ case 52: model.type = e_model::MODEL_20B; break; // granite
+ case 88: model.type = e_model::MODEL_34B; break; // granite
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_MAMBA:
+ {
+ ml.get_key(LLM_KV_SSM_CONV_KERNEL, hparams.ssm_d_conv);
+ ml.get_key(LLM_KV_SSM_INNER_SIZE, hparams.ssm_d_inner);
+ ml.get_key(LLM_KV_SSM_STATE_SIZE, hparams.ssm_d_state);
+ ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
+
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ switch (hparams.n_layer) {
+ case 24:
+ switch (hparams.n_embd) {
+ case 768: model.type = e_model::MODEL_SMALL; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ } break;
+ case 48:
+ switch (hparams.n_embd) {
+ case 1024: model.type = e_model::MODEL_MEDIUM; break;
+ case 1536: model.type = e_model::MODEL_LARGE; break;
+ case 2048: model.type = e_model::MODEL_XL; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ } break;
+ case 64:
+ switch (hparams.n_embd) {
+ case 2560: model.type = e_model::MODEL_3B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ } break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_XVERSE:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ switch (hparams.n_layer) {
+ case 32: model.type = e_model::MODEL_7B; break;
+ case 40: model.type = e_model::MODEL_13B; break;
+ case 80: model.type = e_model::MODEL_65B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_COMMAND_R:
+ {
+ ml.get_key(LLM_KV_LOGIT_SCALE, hparams.f_logit_scale);
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ switch (hparams.n_layer) {
+ case 40: model.type = e_model::MODEL_35B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_DBRX:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv);
+
+ switch (hparams.n_layer) {
+ case 40: model.type = e_model::MODEL_16x12B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_OLMO:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ ml.get_key(LLM_KV_ATTENTION_CLAMP_KQV, hparams.f_clamp_kqv, false);
+
+ switch (hparams.n_layer) {
+ case 22: model.type = e_model::MODEL_1B; break;
+ case 32: model.type = e_model::MODEL_7B; break;
+ case 80: model.type = e_model::MODEL_70B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_OPENELM:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ switch (hparams.n_layer) {
+ case 16: model.type = e_model::MODEL_270M; break;
+ case 20: model.type = e_model::MODEL_450M; break;
+ case 28: model.type = e_model::MODEL_1B; break;
+ case 36: model.type = e_model::MODEL_3B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_GPTNEOX:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ ml.get_key(LLM_KV_USE_PARALLEL_RESIDUAL, hparams.use_par_res);
+ switch (hparams.n_layer) {
+ case 6:
+ switch (hparams.n_ff()) {
+ case 512: model.type = e_model::MODEL_14M; break;
+ case 2048: model.type = e_model::MODEL_70M; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ } break;
+ case 12:
+ switch (hparams.n_ff()) {
+ case 3072: model.type = e_model::MODEL_160M; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ } break;
+ case 16:
+ switch (hparams.n_ff()) {
+ case 8192: model.type = e_model::MODEL_1B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ } break;
+ case 24:
+ switch (hparams.n_ff()) {
+ case 4096: model.type = e_model::MODEL_410M; break;
+ case 8192: model.type = e_model::MODEL_1_4B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ } break;
+ case 32:
+ switch (hparams.n_ff()) {
+ case 10240: model.type = e_model::MODEL_2_8B; break;
+ case 16384: model.type = e_model::MODEL_6_9B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ } break;
+ case 36:
+ switch (hparams.n_ff()) {
+ case 20480: model.type = e_model::MODEL_12B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ } break;
+ case 44:
+ switch (hparams.n_ff()) {
+ case 24576: model.type = e_model::MODEL_20B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ } break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_ARCTIC:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ if (hparams.n_expert == 128) {
+ switch (hparams.n_layer) {
+ case 35: model.type = e_model::MODEL_10B_128x3_66B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } else {
+ model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_DEEPSEEK2:
+ {
+ bool is_lite = (hparams.n_layer == 27);
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead);
+ if (!is_lite) {
+ ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q);
+ }
+ ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv);
+ ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
+ ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
+ ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
+ ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul);
+
+ switch (hparams.n_layer) {
+ case 27: model.type = e_model::MODEL_16B; break;
+ case 60: model.type = e_model::MODEL_236B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_CHATGLM:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ switch (hparams.n_layer) {
+ case 28: model.type = e_model::MODEL_6B; break;
+ case 40: model.type = e_model::MODEL_9B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_BITNET:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ switch (hparams.n_layer) {
+ case 26: model.type = e_model::MODEL_3B; break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_T5:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+ ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
+
+ uint32_t dec_start_token_id;
+ if (ml.get_key(LLM_KV_DECODER_START_TOKEN_ID, dec_start_token_id, false)) {
+ hparams.dec_start_token_id = dec_start_token_id;
+ }
+
+ switch (hparams.n_layer) {
+ case 6: model.type = e_model::MODEL_60M; break; // t5-small
+ case 8: model.type = e_model::MODEL_80M; break; // flan-t5-small
+ case 12:
+ switch (hparams.n_ff()) {
+ case 3072: model.type = e_model::MODEL_220M; break; // t5-base
+ case 2048: model.type = e_model::MODEL_250M; break; // flan-t5-base
+ default: model.type = e_model::MODEL_UNKNOWN;
+ } break;
+ case 24:
+ switch (hparams.n_ff()) {
+ case 4096: model.type = e_model::MODEL_770M; break; // t5-large
+ case 2816: model.type = e_model::MODEL_780M; break; // flan-t5-large
+ case 16384: model.type = e_model::MODEL_3B; break; // t5-3b
+ case 5120: model.type = e_model::MODEL_3B; break; // flan-t5-xl
+ case 65536: model.type = e_model::MODEL_11B; break; // t5-11b
+ case 10240: model.type = e_model::MODEL_11B; break; // flan-t5-xxl
+ default: model.type = e_model::MODEL_UNKNOWN;
+ } break;
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ case LLM_ARCH_JAIS:
+ {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+ ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
+
+ switch (hparams.n_layer) {
+ case 24: model.type = e_model::MODEL_1_3B; break;
+ case 40: model.type = e_model::MODEL_13B; break;
+ /* TODO: add variants */
+ default: model.type = e_model::MODEL_UNKNOWN;
+ }
+ } break;
+ default: (void)0;
+ }
+
+ model.ftype = ml.ftype;
+
+ if (hparams.f_max_alibi_bias > 0.0f) {
+ hparams.use_alibi = true;
+ }
+
+ hparams.rope_type = llama_rope_type(&model);
+}
+
+static void llm_load_vocab(
+ llama_model_loader & ml,
+ llama_model & model) {
+ auto & vocab = model.vocab;
+
+ struct gguf_context * ctx = ml.meta;
+
+ const auto kv = LLM_KV(model.arch);
+
+ // determine vocab type
+ {
+ std::string tokenizer_model;
+ std::string tokenizer_pre;
+
+ ml.get_key(LLM_KV_TOKENIZER_MODEL, tokenizer_model);
+ ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false);
+
+ if (tokenizer_model == "no_vocab") {
+ vocab.type = LLAMA_VOCAB_TYPE_NONE;
+
+ // default special tokens
+ vocab.special_bos_id = -1;
+ vocab.special_eos_id = -1;
+ vocab.special_unk_id = -1;
+ vocab.special_sep_id = -1;
+ vocab.special_pad_id = -1;
+ vocab.special_cls_id = -1;
+ vocab.special_mask_id = -1;
+ vocab.linefeed_id = -1;
+
+ return;
+ } else if (tokenizer_model == "llama") {
+ vocab.type = LLAMA_VOCAB_TYPE_SPM;
+
+ // default special tokens
+ vocab.special_bos_id = 1;
+ vocab.special_eos_id = 2;
+ vocab.special_unk_id = 0;
+ vocab.special_sep_id = -1;
+ vocab.special_pad_id = -1;
+ vocab.special_cls_id = -1;
+ vocab.special_mask_id = -1;
+ } else if (tokenizer_model == "bert") {
+ vocab.type = LLAMA_VOCAB_TYPE_WPM;
+
+ // default special tokens
+ vocab.special_bos_id = -1;
+ vocab.special_eos_id = -1;
+ vocab.special_unk_id = 100;
+ vocab.special_sep_id = 102;
+ vocab.special_pad_id = 0;
+ vocab.special_cls_id = 101;
+ vocab.special_mask_id = 103;
+ } else if (tokenizer_model == "gpt2") {
+ vocab.type = LLAMA_VOCAB_TYPE_BPE;
+
+ // read bpe merges and populate bpe ranks
+ const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str());
+ if (merges_keyidx == -1) {
+ throw std::runtime_error("cannot find tokenizer merges in model file\n");
+ }
+
+ const int n_merges = gguf_get_arr_n(ctx, merges_keyidx);
+ for (int i = 0; i < n_merges; i++) {
+ const std::string word = gguf_get_arr_str(ctx, merges_keyidx, i);
+ GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
+
+ std::string first;
+ std::string second;
+
+ const size_t pos = word.find(' ', 1);
+
+ if (pos != std::string::npos) {
+ first = word.substr(0, pos);
+ second = word.substr(pos + 1);
+ }
+
+ vocab.bpe_ranks.emplace(std::make_pair(first, second), i);
+ }
+
+ // default special tokens
+ vocab.special_bos_id = 11;
+ vocab.special_eos_id = 11;
+ vocab.special_unk_id = -1;
+ vocab.special_sep_id = -1;
+ vocab.special_pad_id = -1;
+ vocab.special_cls_id = -1;
+ vocab.special_mask_id = -1;
+ } else if (tokenizer_model == "t5") {
+ vocab.type = LLAMA_VOCAB_TYPE_UGM;
+
+ // default special tokens
+ vocab.special_bos_id = -1;
+ vocab.special_eos_id = 1;
+ vocab.special_unk_id = 2;
+ vocab.special_sep_id = -1;
+ vocab.special_pad_id = 0;
+ vocab.special_cls_id = -1;
+ vocab.special_mask_id = -1;
+
+ const int precompiled_charsmap_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP).c_str());
+ if (precompiled_charsmap_keyidx != -1) {
+ size_t n_precompiled_charsmap = gguf_get_arr_n(ctx, precompiled_charsmap_keyidx);
+ const char * precompiled_charsmap = (const char *) gguf_get_arr_data(ctx, precompiled_charsmap_keyidx);
+ vocab.precompiled_charsmap.assign(precompiled_charsmap, precompiled_charsmap + n_precompiled_charsmap);
+#ifdef IS_BIG_ENDIAN
+ // correct endiannes of data in precompiled_charsmap binary blob
+ uint32_t * xcda_blob_size = (uint32_t *) &vocab.precompiled_charsmap[0];
+ *xcda_blob_size = __builtin_bswap32(*xcda_blob_size);
+ assert(*xcda_blob_size + sizeof(uint32_t) < n_precompiled_charsmap);
+ size_t xcda_array_size = *xcda_blob_size / sizeof(uint32_t);
+ uint32_t * xcda_array = (uint32_t *) &vocab.precompiled_charsmap[sizeof(uint32_t)];
+ for (size_t i = 0; i < xcda_array_size; ++i) {
+ xcda_array[i] = __builtin_bswap32(xcda_array[i]);
+ }
+#endif
+ }
+ } else {
+ throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str()));
+ }
+
+ // for now, only BPE models have pre-tokenizers
+ if (vocab.type == LLAMA_VOCAB_TYPE_BPE) {
+ vocab.tokenizer_add_space_prefix = false;
+ vocab.tokenizer_clean_spaces = true;
+ if (tokenizer_pre.empty()) {
+ //!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+ // OK - I don't feel like recreati8ng the LLaMA-v3 models. Considering that, at least for now,
+ // LLaMA-v3 is the only model wehere we end up here, let's just force the pre-tokanizer to be
+ // llama3.
+ tokenizer_pre = "llama3";
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
+ LLAMA_LOG_WARN("%s: missing pre-tokenizer type, using: 'llama3'\n", __func__);
+ LLAMA_LOG_WARN("%s: \n", __func__);
+ LLAMA_LOG_WARN("%s: ************************************ \n", __func__);
+ LLAMA_LOG_WARN("%s: GENERATION QUALITY MAY BE DEGRADED! \n", __func__);
+ LLAMA_LOG_WARN("%s: CONSIDER REGENERATING THE MODEL \n", __func__);
+ LLAMA_LOG_WARN("%s: ************************************ \n", __func__);
+ LLAMA_LOG_WARN("%s: \n", __func__);
+ //vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+ //!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
+ } else if (tokenizer_pre == "default") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+ } else if (
+ tokenizer_pre == "llama3" ||
+ tokenizer_pre == "llama-v3" ||
+ tokenizer_pre == "llama-bpe") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_LLAMA3;
+ vocab.tokenizer_ignore_merges = true;
+ vocab.tokenizer_add_bos = true;
+ } else if (
+ tokenizer_pre == "deepseek-llm") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM;
+ vocab.tokenizer_clean_spaces = false;
+ } else if (
+ tokenizer_pre == "deepseek-coder") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER;
+ vocab.tokenizer_clean_spaces = false;
+ } else if (
+ tokenizer_pre == "falcon") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_FALCON;
+ } else if (
+ tokenizer_pre == "mpt") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_MPT;
+ } else if (
+ tokenizer_pre == "starcoder") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STARCODER;
+ } else if (
+ tokenizer_pre == "gpt-2" ||
+ tokenizer_pre == "phi-2" ||
+ tokenizer_pre == "jina-es" ||
+ tokenizer_pre == "jina-de" ||
+ tokenizer_pre == "jina-v2-es" ||
+ tokenizer_pre == "jina-v2-de" ||
+ tokenizer_pre == "jina-v2-code") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2;
+ } else if (
+ tokenizer_pre == "refact") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_REFACT;
+ } else if (
+ tokenizer_pre == "command-r") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_COMMAND_R;
+ vocab.tokenizer_clean_spaces = false;
+ } else if (
+ tokenizer_pre == "qwen2") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2;
+ vocab.tokenizer_clean_spaces = false;
+ } else if (
+ tokenizer_pre == "stablelm2") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STABLELM2;
+ } else if (
+ tokenizer_pre == "olmo") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_OLMO;
+ } else if (
+ tokenizer_pre == "dbrx") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DBRX;
+ } else if (
+ tokenizer_pre == "smaug-bpe") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMAUG;
+ } else if (
+ tokenizer_pre == "poro-chat") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_PORO;
+ vocab.tokenizer_clean_spaces = false;
+ } else if (
+ tokenizer_pre == "chatglm-bpe") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CHATGLM4;
+ vocab.special_bos_id = -1;
+ } else if (
+ tokenizer_pre == "viking") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_VIKING;
+ vocab.tokenizer_clean_spaces = false;
+ } else if (
+ tokenizer_pre == "jais") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_JAIS;
+ } else if (
+ tokenizer_pre == "tekken") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_TEKKEN;
+ vocab.tokenizer_clean_spaces = false;
+ vocab.tokenizer_ignore_merges = true;
+ vocab.tokenizer_add_bos = true;
+ } else if (
+ tokenizer_pre == "smollm") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMOLLM;
+ vocab.tokenizer_clean_spaces = false;
+ } else if (
+ tokenizer_pre == "codeshell") {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_CODESHELL;
+ } else {
+ throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
+ }
+ } else if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+ vocab.tokenizer_add_space_prefix = true;
+ vocab.tokenizer_clean_spaces = false;
+ vocab.tokenizer_add_bos = true;
+ vocab.tokenizer_add_eos = false;
+ } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+ vocab.tokenizer_add_space_prefix = false;
+ vocab.tokenizer_clean_spaces = true;
+ vocab.tokenizer_add_bos = true;
+ vocab.tokenizer_add_eos = false;
+ } else if (vocab.type == LLAMA_VOCAB_TYPE_UGM) {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+ vocab.tokenizer_add_bos = false;
+ vocab.tokenizer_add_eos = true;
+ } else {
+ vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT;
+ }
+
+ ml.get_key(LLM_KV_TOKENIZER_ADD_PREFIX, vocab.tokenizer_add_space_prefix, false);
+ ml.get_key(LLM_KV_TOKENIZER_REMOVE_EXTRA_WS, vocab.tokenizer_remove_extra_whitespaces, false);
+ }
+
+ const int token_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_LIST).c_str());
+ if (token_idx == -1) {
+ throw std::runtime_error("cannot find tokenizer vocab in model file\n");
+ }
+
+ const float * scores = nullptr;
+ const int score_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_SCORES).c_str());
+ if (score_idx != -1) {
+ scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
+ }
+
+ const int * toktypes = nullptr;
+ const int toktype_idx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE).c_str());
+ if (toktype_idx != -1) {
+ toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
+ }
+
+ const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
+
+ vocab.id_to_token.resize(n_vocab);
+
+ for (uint32_t i = 0; i < n_vocab; i++) {
+ std::string word = gguf_get_arr_str(ctx, token_idx, i);
+ GGML_ASSERT(unicode_cpts_from_utf8(word).size() > 0);
+
+ vocab.token_to_id[word] = i;
+ vocab.max_token_len = std::max(vocab.max_token_len, (int) word.size());
+
+ auto & token_data = vocab.id_to_token[i];
+ token_data.text = std::move(word);
+ token_data.score = scores ? scores[i] : 0.0f;
+ token_data.attr = LLAMA_TOKEN_ATTR_NORMAL;
+
+ if (toktypes) { //TODO: remove, required until per token attributes are available from GGUF file
+ switch(toktypes[i]) {
+ case LLAMA_TOKEN_TYPE_UNKNOWN: token_data.attr = LLAMA_TOKEN_ATTR_UNKNOWN; break;
+ case LLAMA_TOKEN_TYPE_UNUSED: token_data.attr = LLAMA_TOKEN_ATTR_UNUSED; break;
+ case LLAMA_TOKEN_TYPE_NORMAL: token_data.attr = LLAMA_TOKEN_ATTR_NORMAL; break;
+ case LLAMA_TOKEN_TYPE_CONTROL: token_data.attr = LLAMA_TOKEN_ATTR_CONTROL; break;
+ case LLAMA_TOKEN_TYPE_USER_DEFINED: token_data.attr = LLAMA_TOKEN_ATTR_USER_DEFINED; break;
+ case LLAMA_TOKEN_TYPE_BYTE: token_data.attr = LLAMA_TOKEN_ATTR_BYTE; break;
+ case LLAMA_TOKEN_TYPE_UNDEFINED: token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED; break;
+ default: token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED; break;
+ }
+ }
+ }
+ GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size());
+
+ // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n'
+ if (vocab.type == LLAMA_VOCAB_TYPE_SPM) {
+ // For Fill-In-the-Middle (FIM)/infill models which where converted
+ // prior to support of FIM special tokens in GGUF, the following
+ // will allow those models to continue to work. The general names
+ // of the known models are currently CodeLlama (LLM_ARCH_LLAMA) and
+ // CodeGemma (LLM_ARCH_GEMMA). This can potentially be removed once
+ // new versions of these models have been published.
+ std::string gen_name;
+ ml.get_key(LLM_KV_GENERAL_NAME, gen_name, false);
+
+ std::transform(gen_name.begin(), gen_name.end(), gen_name.begin(),
+ [](unsigned char c){ return std::tolower(c); });
+
+ if (gen_name.find("code") != std::string::npos) {
+ if (model.arch == LLM_ARCH_LLAMA
+ && 32010 < vocab.id_to_token.size()
+ && vocab.id_to_token[32007].text.find("<PRE>") != std::string::npos
+ && vocab.id_to_token[32008].text.find("<SUF>") != std::string::npos
+ && vocab.id_to_token[32009].text.find("<MID>") != std::string::npos
+ && vocab.id_to_token[32010].text.find("<EOT>") != std::string::npos) {
+ vocab.special_prefix_id = 32007;
+ vocab.special_suffix_id = 32008;
+ vocab.special_middle_id = 32009;
+ vocab.special_eot_id = 32010;
+ } else if (model.arch == LLM_ARCH_GEMMA
+ && 107 < vocab.id_to_token.size()
+ && vocab.id_to_token[67].text == "<|fim_prefix|>"
+ && vocab.id_to_token[69].text == "<|fim_suffix|>"
+ && vocab.id_to_token[68].text == "<|fim_middle|>"
+ && vocab.id_to_token[107].text == "<end_of_turn>") {
+ vocab.special_prefix_id = 67;
+ vocab.special_suffix_id = 69;
+ vocab.special_middle_id = 68;
+ // TODO: this is not EOT, it is "file separator" token, needs fix
+ // https://huggingface.co/google/codegemma-7b-it/blob/9b1d9231388358c04d90bd003458f5070d97db44/tokenizer_config.json#L565-L572
+ //vocab.special_eot_id = 70;
+ vocab.special_eot_id = 107;
+ }
+ }
+ try {
+ vocab.linefeed_id = llama_byte_to_token_impl(vocab, '\n');
+ } catch (const std::exception & e) {
+ LLAMA_LOG_WARN("%s: SPM vocabulary, but newline token not found: %s! Using special_pad_id instead.", __func__, e.what());
+ vocab.linefeed_id = vocab.special_pad_id;
+ }
+ } else if (vocab.type == LLAMA_VOCAB_TYPE_WPM) {
+ vocab.linefeed_id = vocab.special_pad_id;
+ } else {
+ const std::vector<int> ids = llama_tokenize_internal(vocab, "\xC4\x8A", false); // U+010A
+ GGML_ASSERT(!ids.empty() && "model vocab missing newline token");
+ vocab.linefeed_id = ids[0];
+ }
+
+ // special tokens
+ {
+ const std::vector<std::pair<enum llm_kv, int32_t &>> special_token_types = {
+ { LLM_KV_TOKENIZER_BOS_ID, vocab.special_bos_id },
+ { LLM_KV_TOKENIZER_EOS_ID, vocab.special_eos_id },
+ { LLM_KV_TOKENIZER_UNK_ID, vocab.special_unk_id },
+ { LLM_KV_TOKENIZER_SEP_ID, vocab.special_sep_id },
+ { LLM_KV_TOKENIZER_PAD_ID, vocab.special_pad_id },
+ { LLM_KV_TOKENIZER_CLS_ID, vocab.special_cls_id },
+ { LLM_KV_TOKENIZER_MASK_ID, vocab.special_mask_id },
+ { LLM_KV_TOKENIZER_PREFIX_ID, vocab.special_prefix_id },
+ { LLM_KV_TOKENIZER_SUFFIX_ID, vocab.special_suffix_id },
+ { LLM_KV_TOKENIZER_MIDDLE_ID, vocab.special_middle_id },
+ { LLM_KV_TOKENIZER_EOT_ID, vocab.special_eot_id },
+ };
+
+ for (const auto & it : special_token_types) {
+ const std::string & key = kv(std::get<0>(it));
+ int32_t & id = std::get<1>(it);
+
+ uint32_t new_id;
+ if (!ml.get_key(std::get<0>(it), new_id, false)) {
+ continue;
+ }
+ if (new_id >= vocab.id_to_token.size()) {
+ LLAMA_LOG_WARN("%s: bad special token: '%s' = %ud, using default id %d\n",
+ __func__, key.c_str(), new_id, id);
+ } else {
+ id = new_id;
+ }
+ }
+
+ // Handle add_bos_token and add_eos_token
+ {
+ bool temp = true;
+
+ if (ml.get_key(LLM_KV_TOKENIZER_ADD_BOS, temp, false)) {
+ vocab.tokenizer_add_bos = temp;
+ }
+ if (ml.get_key(LLM_KV_TOKENIZER_ADD_EOS, temp, false)) {
+ vocab.tokenizer_add_eos = temp;
+ }
+ }
+
+ // find EOT token: "<|eot_id|>", "<|im_end|>", "<end_of_turn>", etc.
+ //
+ // TODO: convert scripts should provide this token through the KV metadata LLAMA_KV_TOKENIZER_EOT_ID
+ // for now, we apply this workaround to find the EOT token based on its text
+ if (vocab.special_eot_id == -1) {
+ for (const auto & t : vocab.token_to_id) {
+ if (
+ // TODO: gemma "<end_of_turn>" is exported as a normal token, so the following check does not work
+ // need to fix convert script
+ //vocab.id_to_token[t.second].type == LLAMA_TOKEN_TYPE_CONTROL &&
+ (t.first == "<|eot_id|>" ||
+ t.first == "<|im_end|>" ||
+ t.first == "<|end|>" ||
+ t.first == "<end_of_turn>" ||
+ t.first == "<|endoftext|>"
+ )
+ ) {
+ vocab.special_eot_id = t.second;
+ break;
+ }
+ }
+ }
+ }
+
+ // build special tokens cache
+ {
+ for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) {
+ if (vocab.id_to_token[id].attr & (LLAMA_TOKEN_ATTR_CONTROL | LLAMA_TOKEN_ATTR_USER_DEFINED | LLAMA_TOKEN_ATTR_UNKNOWN)) {
+ vocab.cache_special_tokens.push_back(id);
+ }
+ }
+
+ std::sort(vocab.cache_special_tokens.begin(), vocab.cache_special_tokens.end(),
+ [&] (const llama_vocab::id a, const llama_vocab::id b) {
+ return vocab.id_to_token[a].text.size() > vocab.id_to_token[b].text.size();
+ }
+ );
+
+ LLAMA_LOG_INFO("%s: special tokens cache size = %u\n", __func__, (uint32_t)vocab.cache_special_tokens.size());
+ }
+
+ // build token to piece cache
+ {
+ size_t size_cache = 0;
+
+ std::vector<llama_vocab::token> cache_token_to_piece(n_vocab);
+
+ for (uint32_t id = 0; id < n_vocab; ++id) {
+ cache_token_to_piece[id] = llama_token_to_piece(&model, id, true);
+
+ size_cache += cache_token_to_piece[id].size();
+ }
+
+ std::swap(vocab.cache_token_to_piece, cache_token_to_piece);
+
+ LLAMA_LOG_INFO("%s: token to piece cache size = %.4f MB\n", __func__, size_cache / 1024.0 / 1024.0);
+ }
+
+ // Handle per token attributes
+ //NOTE: Each model customizes per token attributes.
+ //NOTE: Per token attributes are missing from the GGUF file.
+ //TODO: Extract attributes from GGUF file.
+ {
+ auto _contains_any = [] (const std::string &str, const std::vector<std::string> &substrs) -> bool {
+ for (auto substr : substrs) {
+ if (str.find(substr) < std::string::npos) {
+ return true;
+ }
+ }
+ return false;
+ };
+
+ auto _set_tokenid_attr = [&] (const llama_vocab::id id, llama_token_attr attr, bool value) {
+ uint32_t current = vocab.id_to_token.at(id).attr;
+ current = value ? (current | attr) : (current & ~attr);
+ vocab.id_to_token[id].attr = (llama_token_attr) current;
+ };
+
+ auto _set_token_attr = [&] (const std::string & token, llama_token_attr attr, bool value) {
+ _set_tokenid_attr(vocab.token_to_id.at(token), attr, value);
+ };
+
+ std::string model_name;
+ std::string tokenizer_pre;
+
+ ml.get_key(LLM_KV_GENERAL_NAME, model_name, false);
+ ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false);
+
+ // model name to lowercase
+ std::transform(model_name.begin(), model_name.end(), model_name.begin(),
+ [] (const std::string::value_type x) {
+ return std::tolower(x);
+ }
+ );
+
+ // set attributes by model/tokenizer name
+ if (_contains_any(tokenizer_pre, {"jina-v2-de", "jina-v2-es", "jina-v2-code"})) {
+ _set_token_attr("<mask>", LLAMA_TOKEN_ATTR_LSTRIP, true);
+ } else if (_contains_any(model_name, {"phi-3", "phi3"})) {
+ for (auto id : vocab.cache_special_tokens) {
+ _set_tokenid_attr(id, LLAMA_TOKEN_ATTR_RSTRIP, true);
+ }
+ for (auto token : {"</s>"}) {
+ _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, true);
+ }
+ for (auto token : {"<unk>", "<s>", "<|endoftext|>"}) {
+ _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, false);
+ }
+ }
+ }
+}
+
+static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
+ const auto & hparams = model.hparams;
+ const auto & vocab = model.vocab;
+
+ const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
+
+ auto print_f = [](const std::function<uint32_t(uint32_t)> & f, uint32_t n) {
+ bool is_var = false;
+
+ std::vector<uint32_t> v;
+ for (uint32_t i = 0; i < n; ++i) {
+ v.push_back(f(i));
+ if (v[i] != v[0]) {
+ is_var = true;
+ }
+ }
+
+ std::stringstream ss;
+
+ if (is_var) {
+ ss << "[";
+ for (uint32_t i = 0; i < n; ++i) {
+ ss << v[i];
+ if (i < n - 1) {
+ ss << ", ";
+ }
+ }
+ ss << "]";
+ } else {
+ ss << v[0];
+ }
+
+ return ss.str();
+ };
+
+ // hparams
+ LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml.fver));
+ LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch));
+ LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, llama_model_vocab_type_name(vocab.type));
+ LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab);
+ LLAMA_LOG_INFO("%s: n_merges = %u\n", __func__, (int) vocab.bpe_ranks.size());
+ LLAMA_LOG_INFO("%s: vocab_only = %d\n", __func__, hparams.vocab_only);
+
+ if (!hparams.vocab_only) {
+ LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train);
+ LLAMA_LOG_INFO("%s: n_embd = %u\n", __func__, hparams.n_embd);
+ LLAMA_LOG_INFO("%s: n_layer = %u\n", __func__, hparams.n_layer);
+ LLAMA_LOG_INFO("%s: n_head = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_head(il); }, hparams.n_layer).c_str());
+ LLAMA_LOG_INFO("%s: n_head_kv = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str());
+ LLAMA_LOG_INFO("%s: n_rot = %u\n", __func__, hparams.n_rot);
+ LLAMA_LOG_INFO("%s: n_swa = %u\n", __func__, hparams.n_swa);
+ LLAMA_LOG_INFO("%s: n_embd_head_k = %u\n", __func__, hparams.n_embd_head_k);
+ LLAMA_LOG_INFO("%s: n_embd_head_v = %u\n", __func__, hparams.n_embd_head_v);
+ LLAMA_LOG_INFO("%s: n_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_gqa(il); }, hparams.n_layer).c_str());
+ LLAMA_LOG_INFO("%s: n_embd_k_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_embd_k_gqa(il); }, hparams.n_layer).c_str());
+ LLAMA_LOG_INFO("%s: n_embd_v_gqa = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_embd_v_gqa(il); }, hparams.n_layer).c_str());
+ LLAMA_LOG_INFO("%s: f_norm_eps = %.1e\n", __func__, hparams.f_norm_eps);
+ LLAMA_LOG_INFO("%s: f_norm_rms_eps = %.1e\n", __func__, hparams.f_norm_rms_eps);
+ LLAMA_LOG_INFO("%s: f_clamp_kqv = %.1e\n", __func__, hparams.f_clamp_kqv);
+ LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n", __func__, hparams.f_max_alibi_bias);
+ LLAMA_LOG_INFO("%s: f_logit_scale = %.1e\n", __func__, hparams.f_logit_scale);
+ LLAMA_LOG_INFO("%s: n_ff = %s\n", __func__, print_f([&](uint32_t il) { return hparams.n_ff(il); }, hparams.n_layer).c_str());
+ LLAMA_LOG_INFO("%s: n_expert = %u\n", __func__, hparams.n_expert);
+ LLAMA_LOG_INFO("%s: n_expert_used = %u\n", __func__, hparams.n_expert_used);
+ LLAMA_LOG_INFO("%s: causal attn = %d\n", __func__, hparams.causal_attn);
+ LLAMA_LOG_INFO("%s: pooling type = %d\n", __func__, hparams.pooling_type);
+ LLAMA_LOG_INFO("%s: rope type = %d\n", __func__, hparams.rope_type);
+ LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type);
+ LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train);
+ LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train);
+ LLAMA_LOG_INFO("%s: n_ctx_orig_yarn = %u\n", __func__, hparams.n_ctx_orig_yarn);
+ LLAMA_LOG_INFO("%s: rope_finetuned = %s\n", __func__, hparams.rope_finetuned ? "yes" : "unknown");
+ LLAMA_LOG_INFO("%s: ssm_d_conv = %u\n", __func__, hparams.ssm_d_conv);
+ LLAMA_LOG_INFO("%s: ssm_d_inner = %u\n", __func__, hparams.ssm_d_inner);
+ LLAMA_LOG_INFO("%s: ssm_d_state = %u\n", __func__, hparams.ssm_d_state);
+ LLAMA_LOG_INFO("%s: ssm_dt_rank = %u\n", __func__, hparams.ssm_dt_rank);
+ }
+
+ LLAMA_LOG_INFO("%s: model type = %s\n", __func__, llama_model_type_name(model.type));
+ LLAMA_LOG_INFO("%s: model ftype = %s\n", __func__, llama_model_ftype_name(model.ftype).c_str());
+ if (ml.n_elements >= 1e12) {
+ LLAMA_LOG_INFO("%s: model params = %.2f T\n", __func__, ml.n_elements*1e-12);
+ } else if (ml.n_elements >= 1e9) {
+ LLAMA_LOG_INFO("%s: model params = %.2f B\n", __func__, ml.n_elements*1e-9);
+ } else if (ml.n_elements >= 1e6) {
+ LLAMA_LOG_INFO("%s: model params = %.2f M\n", __func__, ml.n_elements*1e-6);
+ } else {
+ LLAMA_LOG_INFO("%s: model params = %.2f K\n", __func__, ml.n_elements*1e-3);
+ }
+ if (ml.n_bytes < GiB) {
+ LLAMA_LOG_INFO("%s: model size = %.2f MiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
+ } else {
+ LLAMA_LOG_INFO("%s: model size = %.2f GiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
+ }
+
+ // general kv
+ LLAMA_LOG_INFO("%s: general.name = %s\n", __func__, model.name.c_str());
+
+ // special tokens
+ if (vocab.special_bos_id != -1) { LLAMA_LOG_INFO( "%s: BOS token = %d '%s'\n", __func__, vocab.special_bos_id, vocab.id_to_token[vocab.special_bos_id].text.c_str() ); }
+ if (vocab.special_eos_id != -1) { LLAMA_LOG_INFO( "%s: EOS token = %d '%s'\n", __func__, vocab.special_eos_id, vocab.id_to_token[vocab.special_eos_id].text.c_str() ); }
+ if (vocab.special_unk_id != -1) { LLAMA_LOG_INFO( "%s: UNK token = %d '%s'\n", __func__, vocab.special_unk_id, vocab.id_to_token[vocab.special_unk_id].text.c_str() ); }
+ if (vocab.special_sep_id != -1) { LLAMA_LOG_INFO( "%s: SEP token = %d '%s'\n", __func__, vocab.special_sep_id, vocab.id_to_token[vocab.special_sep_id].text.c_str() ); }
+ if (vocab.special_pad_id != -1) { LLAMA_LOG_INFO( "%s: PAD token = %d '%s'\n", __func__, vocab.special_pad_id, vocab.id_to_token[vocab.special_pad_id].text.c_str() ); }
+ if (vocab.special_cls_id != -1) { LLAMA_LOG_INFO( "%s: CLS token = %d '%s'\n", __func__, vocab.special_cls_id, vocab.id_to_token[vocab.special_cls_id].text.c_str() ); }
+ if (vocab.special_mask_id != -1) { LLAMA_LOG_INFO( "%s: MASK token = %d '%s'\n", __func__, vocab.special_mask_id, vocab.id_to_token[vocab.special_mask_id].text.c_str() ); }
+
+ if (vocab.linefeed_id != -1) { LLAMA_LOG_INFO( "%s: LF token = %d '%s'\n", __func__, vocab.linefeed_id, vocab.id_to_token[vocab.linefeed_id].text.c_str() ); }
+ if (vocab.special_prefix_id != -1) { LLAMA_LOG_INFO( "%s: PRE token = %d '%s'\n", __func__, vocab.special_prefix_id, vocab.id_to_token[vocab.special_prefix_id].text.c_str() ); }
+ if (vocab.special_suffix_id != -1) { LLAMA_LOG_INFO( "%s: SUF token = %d '%s'\n", __func__, vocab.special_suffix_id, vocab.id_to_token[vocab.special_suffix_id].text.c_str() ); }
+ if (vocab.special_middle_id != -1) { LLAMA_LOG_INFO( "%s: MID token = %d '%s'\n", __func__, vocab.special_middle_id, vocab.id_to_token[vocab.special_middle_id].text.c_str() ); }
+ if (vocab.special_eot_id != -1) { LLAMA_LOG_INFO( "%s: EOT token = %d '%s'\n", __func__, vocab.special_eot_id, vocab.id_to_token[vocab.special_eot_id].text.c_str() ); }
+
+ LLAMA_LOG_INFO("%s: max token length = %d\n", __func__, vocab.max_token_len);
+
+ if (model.arch == LLM_ARCH_DEEPSEEK2) {
+ LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead);
+ LLAMA_LOG_INFO("%s: n_lora_q = %d\n", __func__, hparams.n_lora_q);
+ LLAMA_LOG_INFO("%s: n_lora_kv = %d\n", __func__, hparams.n_lora_kv);
+ LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
+ LLAMA_LOG_INFO("%s: n_expert_shared = %d\n", __func__, hparams.n_expert_shared);
+ LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
+ LLAMA_LOG_INFO("%s: rope_yarn_log_mul = %.4f\n", __func__, hparams.rope_yarn_log_mul);
+ }
+
+ if (model.arch == LLM_ARCH_QWEN2MOE) {
+ LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp);
+ LLAMA_LOG_INFO("%s: n_ff_shexp = %d\n", __func__, hparams.n_ff_shexp);
+ }
+}
+
+// Returns false if cancelled by progress_callback
+static bool llm_load_tensors(
+ llama_model_loader & ml,
+ llama_model & model,
+ int n_gpu_layers,
+ enum llama_split_mode split_mode,
+ int main_gpu,
+ const float * tensor_split,
+ bool use_mlock,
+ llama_progress_callback progress_callback,
+ void * progress_callback_user_data) {
+ model.t_start_us = ggml_time_us();
+
+ auto & hparams = model.hparams;
+
+ model.split_mode = split_mode;
+ model.main_gpu = main_gpu;
+ model.n_gpu_layers = n_gpu_layers;
+
+ const int n_layer = hparams.n_layer;
+ const int i_gpu_start = std::max((int) hparams.n_layer - n_gpu_layers, (int) 0);
+ bool use_mmap_buffer = true;
+
+ // there is very little benefit to offloading the input layer, so always keep it on the CPU
+ model.buft_input = llama_default_buffer_type_cpu(true);
+
+ model.buft_layer.resize(n_layer);
+
+ // assign cpu layers
+ for (int i = 0; i < i_gpu_start; ++i) {
+ model.buft_layer[i] = llama_default_buffer_type_cpu(true);
+ }
+
+ if (split_mode == LLAMA_SPLIT_MODE_LAYER) {
+ // calculate the split points
+ int device_count = llama_get_device_count(model);
+ bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; });
+ std::vector<float> splits(device_count);
+ if (all_zero) {
+ // default split, by free memory
+ for (int i = 0; i < device_count; ++i) {
+ splits[i] = llama_get_device_memory(model, i);
+ }
+ } else {
+ std::copy(tensor_split, tensor_split + device_count, splits.begin());
+ }
+
+ // sum and normalize the splits to get the split points
+ float split_sum = 0.0f;
+ for (int i = 0; i < device_count; ++i) {
+ split_sum += splits[i];
+ splits[i] = split_sum;
+ }
+ for (int i = 0; i < device_count; ++i) {
+ splits[i] /= split_sum;
+ }
+
+ // assign the repeating layers to the devices according to the splits
+ int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1);
+ for (int i = i_gpu_start; i < n_layer; ++i) {
+ int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin();
+ model.buft_layer[i] = llama_default_buffer_type_offload(model, layer_gpu);
+ }
+ // assign the output layer
+ if (n_gpu_layers > n_layer) {
+ int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin();
+ model.buft_output = llama_default_buffer_type_offload(model, layer_gpu);
+ } else {
+ model.buft_output = llama_default_buffer_type_cpu(true);
+ }
+ } else {
+ ggml_backend_buffer_type_t split_buft;
+ if (split_mode == LLAMA_SPLIT_MODE_ROW) {
+ split_buft = llama_default_buffer_type_split(model, main_gpu, tensor_split);
+ } else {
+ // LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_LAYER in backends where it is not supported
+ split_buft = llama_default_buffer_type_offload(model, main_gpu);
+ }
+ // assign the repeating layers
+ for (int i = i_gpu_start; i < n_layer; ++i) {
+ model.buft_layer[i] = {
+ split_buft,
+ llama_default_buffer_type_offload(model, main_gpu)
+ };
+ }
+ // assign the output layer
+ if (n_gpu_layers > n_layer) {
+ model.buft_output = {
+ split_buft,
+ llama_default_buffer_type_offload(model, main_gpu)
+ };
+ } else {
+ model.buft_output = llama_default_buffer_type_cpu(true);
+ }
+ }
+
+ // count used buffer types
+ std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
+ buft_layer_count[model.buft_input.buft]++;
+ buft_layer_count[model.buft_input.buft_matrix]++;
+ buft_layer_count[model.buft_output.buft]++;
+ buft_layer_count[model.buft_output.buft_matrix]++;
+ for (int i = 0; i < n_layer; ++i) {
+ buft_layer_count[model.buft_layer[i].buft]++;
+ buft_layer_count[model.buft_layer[i].buft_matrix]++;
+ }
+
+ // create one context per buffer type
+ size_t ctx_size = ggml_tensor_overhead()*(ml.n_tensors + 1); // +1 for models where tok_embd is duplicated as output
+
+ // for moe merged tensors
+ ctx_size += ggml_tensor_overhead()*n_layer*3;
+
+ std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
+ for (auto & it : buft_layer_count) {
+ struct ggml_init_params params = {
+ /*.mem_size =*/ ctx_size,
+ /*.mem_buffer =*/ NULL,
+ /*.no_alloc =*/ true,
+ };
+ ggml_context * ctx = ggml_init(params);
+ if (!ctx) {
+ throw std::runtime_error(format("failed to create context"));
+ }
+ ctx_map[it.first] = ctx;
+ model.ctxs.push_back(ctx);
+ }
+
+ LLAMA_LOG_INFO("%s: ggml ctx size = %7.2f MiB\n", __func__, model.ctxs.size()*ctx_size/1024.0/1024.0);
+
+ // create tensors for the weights
+ {
+ // note: cast to int64_t since we will use these for the tensor dimensions
+ const int64_t n_head = hparams.n_head();
+ const int64_t n_head_kv = hparams.n_head_kv();
+ const int64_t n_embd = hparams.n_embd;
+ const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
+ const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
+ const int64_t n_embd_head_k = hparams.n_embd_head_k;
+ const int64_t n_embd_head_v = hparams.n_embd_head_v;
+ const int64_t n_ff = hparams.n_ff();
+ const int64_t n_embd_gqa = n_embd_v_gqa;
+ const int64_t n_vocab = hparams.n_vocab;
+ const int64_t n_vocab_type = hparams.n_vocab_type;
+ const int64_t n_expert = hparams.n_expert;
+ const int64_t n_expert_used = hparams.n_expert_used;
+ const int64_t n_ctx_train = hparams.n_ctx_train;
+
+ if (n_expert > 0 && hparams.n_expert_used == 0) {
+ throw std::runtime_error("model has expert layers but no expert layers are used");
+ }
+
+ ggml_context * ctx_input = ctx_map.at(model.buft_input.buft);
+ ggml_context * ctx_output = ctx_map.at(model.buft_output.buft);
+ ggml_context * ctx_output_split = ctx_map.at(model.buft_output.buft_matrix);
+
+ auto ctx_for_layer = [&](int i) { return ctx_map.at(model.buft_layer[i].buft); };
+ auto ctx_for_layer_split = [&](int i) { return ctx_map.at(model.buft_layer[i].buft_matrix); };
+
+ model.layers.resize(n_layer);
+
+ const auto tn = LLM_TN(model.arch);
+ switch (model.arch) {
+ case LLM_ARCH_LLAMA:
+ case LLM_ARCH_REFACT:
+ case LLM_ARCH_MINICPM:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ // if output is NULL, init from the input tok embed
+ if (model.output == NULL) {
+ model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+ }
+ }
+
+ 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.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
+ layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
+ layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
+
+ // optional bias tensors
+ layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+
+ if (n_expert == 0) {
+ 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});
+
+ // optional MLP bias
+ layer.ffn_gate_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.ffn_down_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.ffn_up_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ } else {
+ layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
+
+ layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ if (layer.ffn_gate_exps) {
+ layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert});
+ layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
+ } else {
+ // merge split expert into a single tensor for compatibility with older models
+ // requires disabling mmap
+ use_mmap_buffer = false;
+
+ ggml_type type_gate = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, 0).c_str())->type;
+ ggml_type type_down = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, 0).c_str())->type;
+ ggml_type type_up = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, 0).c_str())->type;
+
+ layer.ffn_gate_exps = ggml_new_tensor_3d(ctx_split, type_gate, n_embd, n_ff, n_expert);
+ layer.ffn_down_exps = ggml_new_tensor_3d(ctx_split, type_down, n_ff, n_embd, n_expert);
+ layer.ffn_up_exps = ggml_new_tensor_3d(ctx_split, type_up, n_embd, n_ff, n_expert);
+
+ ggml_set_name(layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i).c_str());
+ ggml_set_name(layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i).c_str());
+ ggml_set_name(layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i).c_str());
+
+ for (uint32_t x = 0; x < n_expert; ++x) {
+ // the individual experts are loaded into a view of the merged tensor
+ ml.create_tensor_as_view(ctx_split, layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_gate_exps->nb[2]*x);
+ ml.create_tensor_as_view(ctx_split, layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd }, layer.ffn_down_exps->nb[2]*x);
+ ml.create_tensor_as_view(ctx_split, layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_up_exps->nb[2]*x);
+ }
+ }
+ }
+ }
+ } break;
+ case LLM_ARCH_GROK:
+ {
+ if (n_expert == 0) {
+ throw std::runtime_error("Grok model cannot have zero experts");
+ }
+
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ // if output is NULL, init from the input tok embed
+ if (model.output == NULL) {
+ model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+ }
+ }
+
+ 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.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.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
+
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+
+ layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
+ layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ if (layer.ffn_gate_exps) {
+ layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert});
+ layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
+ } else {
+ // merge split expert into a single tensor for compatibility with older models
+ // requires disabling mmap
+ use_mmap_buffer = false;
+
+ ggml_type type_gate = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, 0).c_str())->type;
+ ggml_type type_down = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, 0).c_str())->type;
+ ggml_type type_up = ml.require_tensor_meta(tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, 0).c_str())->type;
+
+ layer.ffn_gate_exps = ggml_new_tensor_3d(ctx_split, type_gate, n_embd, n_ff, n_expert);
+ layer.ffn_down_exps = ggml_new_tensor_3d(ctx_split, type_down, n_ff, n_embd, n_expert);
+ layer.ffn_up_exps = ggml_new_tensor_3d(ctx_split, type_up, n_embd, n_ff, n_expert);
+
+ ggml_set_name(layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i).c_str());
+ ggml_set_name(layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i).c_str());
+ ggml_set_name(layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i).c_str());
+
+ for (uint32_t x = 0; x < n_expert; ++x) {
+ // the individual experts are loaded into a view of the merged tensor
+ ml.create_tensor_as_view(ctx_split, layer.ffn_gate_exps, tn(LLM_TENSOR_FFN_GATE_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_gate_exps->nb[2]*x);
+ ml.create_tensor_as_view(ctx_split, layer.ffn_down_exps, tn(LLM_TENSOR_FFN_DOWN_EXP, "weight", i, x), { n_ff, n_embd }, layer.ffn_down_exps->nb[2]*x);
+ ml.create_tensor_as_view(ctx_split, layer.ffn_up_exps, tn(LLM_TENSOR_FFN_UP_EXP, "weight", i, x), { n_embd, n_ff }, layer.ffn_up_exps->nb[2]*x);
+ }
+ }
+
+ layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
+ }
+ } break;
+ case LLM_ARCH_DBRX:
+ {
+ if (n_expert == 0) {
+ throw std::runtime_error("DBRX model cannot have zero experts");
+ }
+
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
+ layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
+
+ layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
+ layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert});
+ layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff, n_embd, n_expert});
+ layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
+ }
+ } break;
+ case LLM_ARCH_BAICHUAN:
+ {
+ 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 = 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.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_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;
+ case LLM_ARCH_FALCON:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ 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}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ if (!model.output) {
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU
+ }
+ }
+
+ 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.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
+ 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;
+ case LLM_ARCH_STARCODER:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+ model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train});
+
+ // output
+ {
+ 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}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ if (!model.output) {
+ // needs to be on GPU
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+ }
+
+ }
+
+ 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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+ layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
+
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+ layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {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_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+ layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
+
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
+ }
+ } break;
+ case LLM_ARCH_BERT:
+ case LLM_ARCH_NOMIC_BERT:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+ model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type});
+
+ if (model.arch == LLM_ARCH_BERT) {
+ model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train});
+ }
+
+ model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
+ model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd});
+
+ 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];
+
+ if (model.arch == LLM_ARCH_BERT) {
+ layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
+ layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
+
+ layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
+ layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
+
+ layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
+ layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
+ } else {
+ layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+ }
+
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+
+ layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd});
+ layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd});
+
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "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});
+
+ if (model.arch == LLM_ARCH_BERT) {
+ layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
+ layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
+ layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
+ } else {
+ layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
+ }
+
+ layer.layer_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
+ layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd});
+ }
+ } break;
+ case LLM_ARCH_JINA_BERT_V2:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // word_embeddings
+ model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); // token_type_embeddings
+
+ model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); // LayerNorm
+ model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); //LayerNorm bias
+
+ 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]; // JinaBertLayer
+
+ layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
+ layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
+
+ layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
+ layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
+
+ layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
+ layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
+
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); //output_dens
+ layer.bo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); //output_dens
+
+ layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); //output_norm
+ layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd});
+
+ layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ 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_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
+
+ layer.layer_out_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
+ layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd});
+ }
+ } break;
+ case LLM_ARCH_BLOOM:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+ model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd});
+ model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd});
+
+ // output
+ {
+ 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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+ layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
+
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+ layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {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_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+ layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
+
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
+ }
+ } break;
+ case LLM_ARCH_MPT:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+ model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ // output
+ {
+ 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}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ if (!model.output) {
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU
+ }
+ }
+
+ 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}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+ layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+ layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ 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}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+ layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ // AWQ ScaleActivation layer
+ layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ }
+ } break;
+ case LLM_ARCH_STABLELM:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd});
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {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});
+
+ // optional bias tensors, present in Stable LM 2 1.6B
+ layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ // optional q and k layernorms, present in StableLM 2 12B
+ layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ // optional FFN norm, not present in StableLM 2 12B which uses parallel residual
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ 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;
+ case LLM_ARCH_QWEN:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd*3});
+ layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd*3});
+ 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_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff/2});
+ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff/2, n_embd});
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff/2});
+ }
+ } break;
+ case LLM_ARCH_QWEN2:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ // if output is NULL, init from the input tok embed
+ if (model.output == NULL) {
+ model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+ }
+ }
+
+ 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.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});
+
+ // optional bias tensors
+ layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
+ layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
+ layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
+
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", 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;
+ case LLM_ARCH_QWEN2MOE:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {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.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});
+
+ // optional bias tensors
+ layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
+ layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
+ layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
+
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+
+ layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
+
+ GGML_ASSERT(n_expert > 0);
+ GGML_ASSERT(n_expert_used > 0);
+
+ // MoE branch
+ const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+
+ layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
+ layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert});
+ layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
+
+ // Shared expert branch
+ const int64_t n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
+
+ layer.ffn_gate_inp_shexp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd});
+ layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), { n_embd, n_ff_shexp});
+ layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd});
+ layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), { n_embd, n_ff_shexp});
+ }
+ } break;
+ case LLM_ARCH_PHI2:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ 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});
+ model.output_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT, "bias"), {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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ if (layer.wqkv == nullptr) {
+ layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
+ layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
+
+ layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
+ layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
+
+ layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
+ layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
+ }
+
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+ layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd});
+
+ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+ layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
+
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
+ }
+ } break;
+ case LLM_ARCH_PHI3:
+ {
+ const int64_t n_embd_head = n_embd / n_head;
+
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab });
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { 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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED);
+ 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_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, 2 * n_ff });
+
+ layer.rope_long = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+ layer.rope_short = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
+ }
+ } break;
+ case LLM_ARCH_PLAMO:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {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.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_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;
+ case LLM_ARCH_GPT2:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+ model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, n_ctx_train});
+
+ // output
+ {
+ 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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+ layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
+
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+ layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {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_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+ layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
+
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
+ }
+ } break;
+ case LLM_ARCH_CODESHELL:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ 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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+ layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
+
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+ layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {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_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+ layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
+
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ 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;
+ case LLM_ARCH_INTERNLM2:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+ 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_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;
+ case LLM_ARCH_GEMMA:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+ model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
+
+ 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.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
+ layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
+ layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
+
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+ layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "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});
+ }
+ } break;
+ case LLM_ARCH_GEMMA2:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+ model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
+
+ 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.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head});
+ layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
+ layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
+ layer.attn_post_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd});
+
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+ layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "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_post_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_POST_NORM, "weight", i), {n_embd});
+ }
+ } break;
+ case LLM_ARCH_STARCODER2:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ 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}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ // if output is NULL, init from the input tok embed
+ if (model.output == NULL) {
+ model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+ }
+
+ }
+
+ 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});
+
+ // optional bias tensors
+ layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
+ layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
+ layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
+ layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {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_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});
+
+ // optional bias tensors
+ layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
+ layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP , "bias", i), { n_ff});
+ }
+ } break;
+ case LLM_ARCH_MAMBA:
+ {
+ const int64_t d_conv = hparams.ssm_d_conv;
+ const int64_t d_inner = hparams.ssm_d_inner;
+ const int64_t d_state = hparams.ssm_d_state;
+ const int64_t dt_rank = hparams.ssm_dt_rank;
+
+ // only an expansion factor of 2 is supported for now
+ GGML_ASSERT(2 * n_embd == d_inner);
+
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ // if output is NULL, init from the input tok embed, duplicated to allow offloading
+ if (model.output == NULL) {
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+ }
+ }
+
+ 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];
+
+ // norm
+ layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
+ layer.ssm_in = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_IN, "weight", i), {n_embd, 2*d_inner});
+
+ layer.ssm_conv1d = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_CONV1D, "weight", i), {d_conv, d_inner});
+ layer.ssm_conv1d_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_CONV1D, "bias", i), {d_inner});
+
+ layer.ssm_x = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_X, "weight", i), {d_inner, dt_rank + 2*d_state});
+
+ layer.ssm_dt = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_DT, "weight", i), {dt_rank, d_inner});
+ layer.ssm_dt_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_DT, "bias", i), {d_inner});
+
+ // no "weight" suffix for these
+ layer.ssm_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_A, i), {d_state, d_inner});
+ layer.ssm_d = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_SSM_D, i), {d_inner});
+
+ // out_proj
+ layer.ssm_out = ml.create_tensor(ctx_split, tn(LLM_TENSOR_SSM_OUT, "weight", i), {d_inner, n_embd});
+ }
+ } break;
+ case LLM_ARCH_XVERSE:
+ {
+ 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 = 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.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_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;
+ case LLM_ARCH_COMMAND_R:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+ // init output from the input tok embed
+ model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+ }
+
+ 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});
+
+ if (n_layer >= 64){
+ layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k, n_head});
+ layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k, n_head_kv});
+ }
+
+ 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_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;
+ case LLM_ARCH_OLMO: // adapted from LLM_ARCH_LLAMA with norm params removed
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ // if output is NULL, init from the input tok embed
+ if (model.output == NULL) {
+ model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+ }
+ }
+
+ for (int i = 0; i < n_layer; ++i) {
+ ggml_context * ctx_split = ctx_for_layer_split(i);
+
+ auto & layer = model.layers[i];
+
+ 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_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;
+ case LLM_ARCH_OPENELM:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+ // init output from the input tok embed
+ model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+ }
+
+ for (int i = 0; i < n_layer; ++i) {
+ const int64_t n_head = hparams.n_head(i);
+ const int64_t n_head_qkv = 2*hparams.n_head_kv(i) + n_head;
+ const int64_t n_ff = hparams.n_ff(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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head_k});
+ layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k});
+ layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k});
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head_k, n_embd});
+
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", 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;
+ case LLM_ARCH_GPTNEOX:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ 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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+ layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
+
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+ layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {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_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+ layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
+
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
+ }
+ } break;
+ case LLM_ARCH_ARCTIC:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ // if output is NULL, init from the input tok embed
+ if (model.output == NULL) {
+ model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+ }
+ }
+
+ 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.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_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_embd});
+ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd});
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_embd});
+
+ layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
+ layer.ffn_norm_exps = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd});
+ layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false);
+ layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert});
+ layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert});
+ }
+ } break;
+ case LLM_ARCH_DEEPSEEK2:
+ {
+ const bool is_lite = (hparams.n_layer == 27);
+
+ const int64_t n_embd_head_qk_rope = hparams.n_rot;
+ const int64_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
+
+ const int64_t q_lora_rank = hparams.n_lora_q;
+ const int64_t kv_lora_rank = hparams.n_lora_kv;
+
+ const int64_t n_ff_exp = hparams.n_ff_exp;
+ const int64_t n_expert_shared = hparams.n_expert_shared;
+
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {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});
+ if (!is_lite) {
+ layer.attn_q_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank});
+ }
+
+ layer.attn_kv_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank});
+
+ if (!is_lite) {
+ layer.wq_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank});
+ layer.wq_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, n_head * n_embd_head_k});
+ } else {
+ layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
+ }
+
+ layer.wkv_a_mqa = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + (n_embd_head_qk_rope)});
+ layer.wkv_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)});
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_head * ( n_embd_head_v), n_embd});
+
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+
+ if (i < (int) hparams.n_layer_dense_lead) {
+ 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});
+ } else {
+ layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
+
+ GGML_ASSERT(n_expert > 0);
+ GGML_ASSERT(n_expert_used > 0);
+
+ // MoE branch
+ layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
+ layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert});
+ layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert});
+
+ // Shared expert branch
+ layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared});
+ layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * n_expert_shared, n_embd});
+ layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * n_expert_shared});
+ }
+ }
+ } break;
+ case LLM_ARCH_BITNET:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+ model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
+ }
+
+ const uint32_t n_ff = hparams.n_ff();
+ model.layers.resize(n_layer);
+ 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_sub_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_SUB_NORM, "weight", i), {n_embd});
+
+ layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
+ layer.wq_scale = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "scale", i), {1});
+ layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
+ layer.wk_scale = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "scale", i), {1});
+ layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
+ layer.wv_scale = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "scale", i), {1});
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+ layer.wo_scale = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "scale", i), {1});
+
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+ layer.ffn_sub_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_SUB_NORM, "weight", i), {n_ff});
+
+ layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
+ layer.ffn_gate_scale = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "scale", i), {1});
+ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+ layer.ffn_down_scale = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "scale", i), {1});
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ layer.ffn_up_scale = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "scale", i), {1});
+ }
+ } break;
+ case LLM_ARCH_T5:
+ {
+ const auto n_rel_attn_bkts = hparams.n_rel_attn_bkts;
+
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm_enc = ml.create_tensor(ctx_output, tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd});
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd});
+
+ model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ // if output is NULL, init from the input tok embed
+ if (model.output == NULL) {
+ model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+ }
+ }
+
+ 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_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_ATTN_NORM, "weight", i), {n_embd});
+ layer.attn_rel_b_enc = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ layer.wq_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
+ layer.wk_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
+ layer.wv_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
+ layer.wo_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
+
+ layer.ffn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd});
+ layer.ffn_gate_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.ffn_down_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), { n_ff, n_embd});
+ layer.ffn_up_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_UP, "weight", i), {n_embd, n_ff});
+
+ layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_ATTN_NORM, "weight", i), {n_embd});
+ layer.attn_rel_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
+ layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
+ layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
+
+ layer.attn_norm_cross = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM, "weight", i), {n_embd});
+ // this tensor seems to be unused in HF transformers implementation
+ layer.attn_rel_b_cross = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {n_head, n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+ layer.wq_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa});
+ layer.wk_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_K, "weight", i), {n_embd, n_embd_k_gqa});
+ layer.wv_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_V, "weight", i), {n_embd, n_embd_v_gqa});
+ layer.wo_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
+
+ layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd});
+ layer.ffn_gate = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd, n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
+ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_FFN_DOWN, "weight", i), { n_ff, n_embd});
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_FFN_UP, "weight", i), {n_embd, n_ff});
+ }
+ } break;
+ case LLM_ARCH_JAIS:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // Output
+ {
+ 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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+ layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa});
+
+ layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+ layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {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_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+ layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "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_gate_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff});
+
+ layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
+ layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff});
+ }
+ } break;
+ case LLM_ARCH_CHATGLM:
+ {
+ model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+ // output
+ {
+ model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + (hparams.n_embd_head_k << 2)});
+ layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + (hparams.n_embd_head_k << 2)});
+
+ 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_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff * 2});
+
+ layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+ }
+ } break;
+ default:
+ throw std::runtime_error("unknown architecture");
+ }
+ }
+
+ ml.done_getting_tensors();
+
+ ml.init_mappings(true, use_mlock ? &model.mlock_mmaps : nullptr);
+ model.mappings.reserve(ml.mappings.size());
+
+ // create the backend buffers
+ std::vector<std::pair<ggml_context *, llama_buf_map>> ctx_bufs;
+ ctx_bufs.reserve(ctx_map.size());
+
+ // Ensure we have enough capacity for the maximum backend buffer we will potentially create
+ size_t n_max_backend_buffer = ctx_map.size() * ml.files.size();
+ model.bufs.reserve(n_max_backend_buffer);
+
+ for (auto & it : ctx_map) {
+ ggml_backend_buffer_type_t buft = it.first;
+ ggml_context * ctx = it.second;
+
+ llama_buf_map bufs;
+ bufs.reserve(n_max_backend_buffer);
+
+ // only the mmap region containing the tensors in the model is mapped to the backend buffer
+ // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
+ // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
+ if (ml.use_mmap && use_mmap_buffer && buft == llama_default_buffer_type_cpu(true)) {
+ for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
+ void * addr = nullptr;
+ size_t first, last;
+ ml.get_mapping_range(&first, &last, &addr, idx, ctx);
+ if (first >= last) {
+ continue;
+ }
+ ggml_backend_buffer_t buf = ggml_backend_cpu_buffer_from_ptr((char *) addr + first, last - first);
+ if (buf == nullptr) {
+ throw std::runtime_error("unable to allocate backend CPU buffer");
+ }
+ model.bufs.push_back(buf);
+ bufs.emplace(idx, buf);
+#ifdef GGML_USE_CUDA
+ if (n_layer >= n_gpu_layers) {
+ ggml_backend_cuda_register_host_buffer(
+ ggml_backend_buffer_get_base(buf),
+ ggml_backend_buffer_get_size(buf));
+ }
+#endif
+ }
+ }
+#ifdef GGML_USE_METAL
+ else if (ml.use_mmap && use_mmap_buffer && buft == ggml_backend_metal_buffer_type()) {
+ for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
+ const size_t max_size = ggml_get_max_tensor_size(ctx);
+ void * addr = nullptr;
+ size_t first, last;
+ ml.get_mapping_range(&first, &last, &addr, idx, ctx);
+ if (first >= last) {
+ continue;
+ }
+ ggml_backend_buffer_t buf = ggml_backend_metal_buffer_from_ptr((char *) addr + first, last - first, max_size);
+ if (buf == nullptr) {
+ throw std::runtime_error("unable to allocate backend metal buffer");
+ }
+ model.bufs.push_back(buf);
+ bufs.emplace(idx, buf);
+ }
+ }
+#endif
+ else {
+ ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
+ if (buf == nullptr) {
+ throw std::runtime_error("unable to allocate backend buffer");
+ }
+ model.bufs.push_back(buf);
+ if (use_mlock && ggml_backend_buffer_is_host(buf)) {
+ model.mlock_bufs.emplace_back(new llama_mlock);
+ auto & mlock_buf = model.mlock_bufs.back();
+ mlock_buf->init (ggml_backend_buffer_get_base(buf));
+ mlock_buf->grow_to(ggml_backend_buffer_get_size(buf));
+ }
+ for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
+ bufs.emplace(idx, buf);
+ }
+ }
+
+ if (bufs.empty()) {
+ throw std::runtime_error("failed to allocate buffer");
+ }
+
+ for (auto & buf : bufs) {
+ // indicate that this buffer contains weights
+ // this is used by ggml_backend_sched to improve op scheduling -> ops that use a weight are preferably scheduled to the backend that contains the weight
+ ggml_backend_buffer_set_usage(buf.second, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
+ }
+
+ ctx_bufs.emplace_back(ctx, bufs);
+ }
+
+ if (llama_supports_gpu_offload()) {
+ const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
+
+ LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_gpu);
+ if (n_gpu_layers > (int) hparams.n_layer) {
+ LLAMA_LOG_INFO("%s: offloading non-repeating layers to GPU\n", __func__);
+ }
+
+ const int max_backend_supported_layers = hparams.n_layer + 1;
+ const int max_offloadable_layers = hparams.n_layer + 1;
+
+ LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
+ }
+
+ // print memory requirements
+ for (ggml_backend_buffer_t buf : model.bufs) {
+ LLAMA_LOG_INFO("%s: %10s buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0);
+ }
+
+ // populate tensors_by_name
+ for (ggml_context * ctx : model.ctxs) {
+ for (auto * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
+ model.tensors_by_name.emplace_back(ggml_get_name(cur), cur);
+ }
+ }
+
+ // load tensor data
+ for (auto & it : ctx_bufs) {
+ ggml_context * ctx = it.first;
+ auto & bufs = it.second;
+ if (!ml.load_all_data(ctx, bufs, use_mlock ? &model.mlock_mmaps : NULL, progress_callback, progress_callback_user_data)) {
+ return false;
+ }
+ }
+
+ if (use_mmap_buffer) {
+ for (auto & mapping : ml.mappings) {
+ model.mappings.emplace_back(std::move(mapping));
+ }
+ }
+
+ if (model.arch == LLM_ARCH_BITNET) {
+ auto set_scale = [] (ggml_tensor * w, ggml_tensor * s) {
+ float scale = 1;
+ if (ggml_backend_buffer_is_host(s->buffer)) {
+ scale = *(const float *)s->data;
+ } else {
+ ggml_backend_tensor_get(s, &scale, 0, sizeof(float));
+ }
+ std::memcpy(w->op_params, &scale, sizeof(scale));
+ };
+ for (auto& l : model.layers) {
+ set_scale(l.ffn_up, l.ffn_up_scale);
+ set_scale(l.ffn_gate, l.ffn_gate_scale);
+ set_scale(l.ffn_down, l.ffn_down_scale);
+ set_scale(l.wq, l.wq_scale);
+ set_scale(l.wk, l.wk_scale);
+ set_scale(l.wv, l.wv_scale);
+ set_scale(l.wo, l.wo_scale);
+ }
+ }
+
+ // loading time will be recalculate after the first eval, so
+ // we take page faults deferred by mmap() into consideration
+ model.t_load_us = ggml_time_us() - model.t_start_us;
+ return true;
+}
+
+// Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback
+static int llama_model_load(const std::string & fname, llama_model & model, llama_model_params & params) {
+ try {
+ llama_model_loader ml(fname, params.use_mmap, params.check_tensors, params.kv_overrides);
+
+ model.hparams.vocab_only = params.vocab_only;
+
+ try {
+ llm_load_arch(ml, model);
+ } catch(const std::exception & e) {
+ throw std::runtime_error("error loading model architecture: " + std::string(e.what()));
+ }
+ try {
+ llm_load_hparams(ml, model);
+ } catch(const std::exception & e) {
+ throw std::runtime_error("error loading model hyperparameters: " + std::string(e.what()));
+ }
+ try {
+ llm_load_vocab(ml, model);
+ } catch(const std::exception & e) {
+ throw std::runtime_error("error loading model vocabulary: " + std::string(e.what()));
+ }
+
+ llm_load_print_meta(ml, model);
+
+ if (model.vocab.type != LLAMA_VOCAB_TYPE_NONE &&
+ model.hparams.n_vocab != model.vocab.id_to_token.size()) {
+ throw std::runtime_error("vocab size mismatch");
+ }
+
+ if (params.vocab_only) {
+ LLAMA_LOG_INFO("%s: vocab only - skipping tensors\n", __func__);
+ return 0;
+ }
+
+#ifdef GGML_USE_KOMPUTE
+ if (params.n_gpu_layers > 0 && (
+ !(model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON)
+ || !(
+ model.ftype == LLAMA_FTYPE_ALL_F32 ||
+ model.ftype == LLAMA_FTYPE_MOSTLY_F16 ||
+ model.ftype == LLAMA_FTYPE_MOSTLY_BF16 ||
+ model.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ||
+ model.ftype == LLAMA_FTYPE_MOSTLY_Q4_1
+ )
+ )) {
+ // TODO(cebtenzzre): propagate this error outside of llama_load_model_from_file
+ LLAMA_LOG_WARN("%s: disabling Kompute due to unsupported model arch or quantization\n", __func__);
+ params.n_gpu_layers = 0;
+ }
+#endif
+
+ if (!llm_load_tensors(
+ ml, model, params.n_gpu_layers, params.split_mode, params.main_gpu, params.tensor_split, params.use_mlock,
+ params.progress_callback, params.progress_callback_user_data
+ )) {
+ return -2;
+ }
+ } catch (const std::exception & err) {
+ LLAMA_LOG_ERROR("%s: error loading model: %s\n", __func__, err.what());
+ return -1;
+ }
+
+ return 0;
+}
+
+//
+// llm_build
+//
+
+using llm_build_cb = std::function<void(struct ggml_tensor * cur, const char * name, int nl)>;
+
+enum llm_ffn_op_type {
+ LLM_FFN_SILU,
+ LLM_FFN_GELU,
+ LLM_FFN_RELU,
+ LLM_FFN_RELU_SQR,
+ LLM_FFN_SWIGLU,
+};
+
+enum llm_ffn_gate_type {
+ LLM_FFN_SEQ,
+ LLM_FFN_PAR, // ffn_gate is parallel to ffn_up
+};
+
+enum llm_norm_type {
+ LLM_NORM,
+ LLM_NORM_RMS,
+};
+
+static struct ggml_tensor * llm_build_inp_embd(
+ struct ggml_context * ctx,
+ struct llama_context & lctx,
+ const llama_hparams & hparams,
+ const llama_batch & batch,
+ struct ggml_tensor * tok_embd,
+ const llm_build_cb & cb) {
+ const int64_t n_embd = hparams.n_embd;
+
+ struct ggml_tensor * inpL;
+
+ if (batch.token) {
+ lctx.inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens);
+ cb(lctx.inp_tokens, "inp_tokens", -1);
+ ggml_set_input(lctx.inp_tokens);
+
+ inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens);
+ } else {
+ lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens);
+ inpL = lctx.inp_embd;
+ ggml_set_input(lctx.inp_embd);
+ }
+
+ cb(inpL, "inp_embd", -1);
+
+ return inpL;
+}
+
+static void llm_build_kv_store(
+ struct ggml_context * ctx,
+ const llama_hparams & hparams,
+ const llama_cparams & cparams,
+ const llama_kv_cache & kv,
+ struct ggml_cgraph * graph,
+ struct ggml_tensor * k_cur,
+ struct ggml_tensor * v_cur,
+ int32_t n_tokens,
+ int32_t kv_head,
+ const llm_build_cb & cb,
+ int64_t il) {
+ const int64_t n_ctx = cparams.n_ctx;
+
+ const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
+ const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
+
+ GGML_ASSERT(kv.size == n_ctx);
+
+ struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_k_gqa,
+ (ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa))*kv_head);
+ cb(k_cache_view, "k_cache_view", il);
+
+ // note: storing RoPE-ed version of K in the KV cache
+ ggml_build_forward_expand(graph, ggml_cpy(ctx, k_cur, k_cache_view));
+
+ assert(v_cur->ne[0] == n_embd_v_gqa && v_cur->ne[1] == n_tokens);
+
+ struct ggml_tensor * v_cache_view = nullptr;
+
+ if (cparams.flash_attn) {
+ v_cache_view = ggml_view_1d(ctx, kv.v_l[il], n_tokens*n_embd_v_gqa,
+ (kv_head)*ggml_row_size(kv.v_l[il]->type, n_embd_v_gqa));
+ } else {
+ // note: the V cache is transposed when not using flash attention
+ v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_v_gqa,
+ ( n_ctx)*ggml_element_size(kv.v_l[il]),
+ (kv_head)*ggml_element_size(kv.v_l[il]));
+
+ v_cur = ggml_transpose(ctx, v_cur);
+ }
+ cb(v_cache_view, "v_cache_view", il);
+
+ ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur, v_cache_view));
+}
+
+// do mat_mul, while optionally apply lora
+static struct ggml_tensor * llm_build_lora_mm(
+ struct llama_context & lctx,
+ struct ggml_context * ctx0,
+ struct ggml_tensor * w,
+ struct ggml_tensor * cur) {
+ struct ggml_tensor * res = ggml_mul_mat(ctx0, w, cur);
+ for (auto & it : lctx.lora_adapters) {
+ struct llama_lora_weight * lora = it.first->get_weight(w);
+ if (lora == nullptr) {
+ continue;
+ }
+ const float alpha = it.first->alpha;
+ const float rank = (float) lora->b->ne[0];
+ const float scale = alpha ? it.second * alpha / rank : it.second;
+ struct ggml_tensor * ab_cur = ggml_mul_mat(
+ ctx0, lora->b,
+ ggml_mul_mat(ctx0, lora->a, cur)
+ );
+ ab_cur = ggml_scale(ctx0, ab_cur, scale);
+ res = ggml_add(ctx0, res, ab_cur);
+ }
+ return res;
+}
+
+// do mat_mul_id, while optionally apply lora
+static struct ggml_tensor * llm_build_lora_mm_id(
+ struct llama_context & lctx,
+ struct ggml_context * ctx0,
+ struct ggml_tensor * w, // struct ggml_tensor * as
+ struct ggml_tensor * cur, // struct ggml_tensor * b
+ struct ggml_tensor * ids) {
+ struct ggml_tensor * res = ggml_mul_mat_id(ctx0, w, cur, ids);
+ for (auto & it : lctx.lora_adapters) {
+ struct llama_lora_weight * lora = it.first->get_weight(w);
+ if (lora == nullptr) {
+ continue;
+ }
+ const float alpha = it.first->alpha;
+ const float rank = (float) lora->b->ne[0];
+ const float scale = alpha ? it.second * alpha / rank : it.second;
+ struct ggml_tensor * ab_cur = ggml_mul_mat_id(
+ ctx0, lora->b,
+ ggml_mul_mat_id(ctx0, lora->a, cur, ids),
+ ids
+ );
+ ab_cur = ggml_scale(ctx0, ab_cur, scale);
+ res = ggml_add(ctx0, res, ab_cur);
+ }
+ return res;
+}
+
+static struct ggml_tensor * llm_build_norm(
+ struct ggml_context * ctx,
+ struct ggml_tensor * cur,
+ const llama_hparams & hparams,
+ struct ggml_tensor * mw,
+ struct ggml_tensor * mb,
+ llm_norm_type type,
+ const llm_build_cb & cb,
+ int il, float scale_eps = 1) {
+ switch (type) {
+ case LLM_NORM: cur = ggml_norm (ctx, cur, hparams.f_norm_eps); break;
+ case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, scale_eps * hparams.f_norm_rms_eps); break;
+ }
+
+ if (mw || mb) {
+ cb(cur, "norm", il);
+ }
+
+ if (mw) {
+ cur = ggml_mul(ctx, cur, mw);
+ if (mb) {
+ cb(cur, "norm_w", il);
+ }
+ }
+
+ if (mb) {
+ cur = ggml_add(ctx, cur, mb);
+ }
+
+ return cur;
+}
+
+static struct ggml_tensor * llm_build_ffn(
+ struct ggml_context * ctx,
+ struct llama_context & lctx,
+ struct ggml_tensor * cur,
+ struct ggml_tensor * up,
+ struct ggml_tensor * up_b,
+ struct ggml_tensor * up_s,
+ struct ggml_tensor * gate,
+ struct ggml_tensor * gate_b,
+ struct ggml_tensor * gate_s,
+ struct ggml_tensor * down,
+ struct ggml_tensor * down_b,
+ struct ggml_tensor * down_s,
+ struct ggml_tensor * act_scales,
+ llm_ffn_op_type type_op,
+ llm_ffn_gate_type type_gate,
+ const llm_build_cb & cb,
+ int il) {
+ struct ggml_tensor * tmp = up ? llm_build_lora_mm(lctx, ctx, up, cur) : cur;
+ cb(tmp, "ffn_up", il);
+
+ if (up_b) {
+ tmp = ggml_add(ctx, tmp, up_b);
+ cb(tmp, "ffn_up_b", il);
+ }
+
+ if (up_s) {
+ tmp = ggml_mul(ctx, tmp, up_s);
+ cb(tmp, "ffn_up_s", il);
+ }
+
+ if (gate) {
+ switch (type_gate) {
+ case LLM_FFN_SEQ:
+ {
+ cur = llm_build_lora_mm(lctx, ctx, gate, tmp);
+ cb(cur, "ffn_gate", il);
+ } break;
+ case LLM_FFN_PAR:
+ {
+ cur = llm_build_lora_mm(lctx, ctx, gate, cur);
+ cb(cur, "ffn_gate", il);
+ } break;
+ }
+
+ if (gate_b) {
+ cur = ggml_add(ctx, cur, gate_b);
+ cb(cur, "ffn_gate_b", il);
+ }
+
+ if (gate_s) {
+ cur = ggml_mul(ctx, cur, gate_s);
+ cb(cur, "ffn_gate_s", il);
+ }
+
+ } else {
+ cur = tmp;
+ }
+
+ switch (type_op) {
+ case LLM_FFN_SILU:
+ {
+ cur = ggml_silu(ctx, cur);
+ cb(cur, "ffn_silu", il);
+ } break;
+ case LLM_FFN_GELU:
+ {
+ cur = ggml_gelu(ctx, cur);
+ cb(cur, "ffn_gelu", il);
+ if (act_scales != NULL) {
+ cur = ggml_div(ctx, cur, act_scales);
+ cb(cur, "ffn_act", il);
+ }
+ } break;
+ case LLM_FFN_RELU:
+ {
+ cur = ggml_relu(ctx, cur);
+ cb(cur, "ffn_relu", il);
+ } break;
+ case LLM_FFN_RELU_SQR:
+ {
+ cur = ggml_relu(ctx, cur);
+ cb(cur, "ffn_relu", il);
+
+ cur = ggml_sqr(ctx, cur);
+ cb(cur, "ffn_sqr(relu)", il);
+ } break;
+ case LLM_FFN_SWIGLU:
+ {
+ // Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf
+ int64_t split_point = cur->ne[0] / 2;
+ struct ggml_tensor * x0 = ggml_cont(ctx, ggml_view_2d(ctx, cur, split_point, cur->ne[1], cur->nb[1], 0));
+ struct ggml_tensor * x1 = ggml_cont(ctx, ggml_view_2d(ctx, cur, split_point, cur->ne[1], cur->nb[1], split_point * ggml_element_size(cur)));
+
+ x0 = ggml_silu(ctx, x0);
+ cb(cur, "ffn_silu", il);
+
+ cur = ggml_mul(ctx, x0, x1);
+ cb(cur, "ffn_mul", il);
+ } break;
+ }
+
+ if (type_gate == LLM_FFN_PAR) {
+ cur = ggml_mul(ctx, cur, tmp);
+ cb(cur, "ffn_gate_par", il);
+ }
+
+ if (down) {
+ cur = llm_build_lora_mm(lctx, ctx, down, cur);
+ }
+
+ if (down_b) {
+ cb(cur, "ffn_down", il);
+ }
+
+ if (down_b) {
+ cur = ggml_add(ctx, cur, down_b);
+ }
+
+ if (down_s) {
+ cur = ggml_mul(ctx, cur, down_s);
+ cb(cur, "ffn_down_s", il);
+ }
+
+ return cur;
+}
+
+static struct ggml_tensor * llm_build_moe_ffn(
+ struct ggml_context * ctx,
+ struct llama_context & lctx,
+ struct ggml_tensor * cur,
+ struct ggml_tensor * gate_inp,
+ struct ggml_tensor * up_exps,
+ struct ggml_tensor * gate_exps,
+ struct ggml_tensor * down_exps,
+ int64_t n_expert,
+ int64_t n_expert_used,
+ llm_ffn_op_type type_op,
+ bool norm_w,
+ bool scale_w,
+ float w_scale,
+ const llm_build_cb & cb,
+ int il) {
+ int64_t n_embd = cur->ne[0];
+ int64_t n_tokens = cur->ne[1];
+
+ ggml_tensor * logits = llm_build_lora_mm(lctx, ctx, gate_inp, cur); // [n_expert, n_tokens]
+ cb(logits, "ffn_moe_logits", il);
+
+ ggml_tensor * probs = ggml_soft_max(ctx, logits); // [n_expert, n_tokens]
+ cb(probs, "ffn_moe_probs", il);
+
+ // select experts
+ ggml_tensor * selected_experts = ggml_top_k(ctx, probs, n_expert_used); // [n_expert_used, n_tokens]
+ cb(selected_experts->src[0], "ffn_moe_argsort", il);
+ cb(selected_experts, "ffn_moe_topk", il);
+
+ ggml_tensor * weights = ggml_get_rows(ctx,
+ ggml_reshape_3d(ctx, probs, 1, n_expert, n_tokens), selected_experts); // [1, n_expert_used, n_tokens]
+ cb(weights, "ffn_moe_weights", il);
+
+ if (norm_w) {
+ weights = ggml_reshape_2d(ctx, weights, n_expert_used, n_tokens);
+
+ ggml_tensor * weights_sum = ggml_sum_rows(ctx, weights); // [1, n_tokens]
+ cb(weights_sum, "ffn_moe_weights_sum", il);
+
+ weights = ggml_div(ctx, weights, weights_sum); // [n_expert_used, n_tokens]
+ cb(weights, "ffn_moe_weights_norm", il);
+
+ weights = ggml_reshape_3d(ctx, weights, 1, n_expert_used, n_tokens);
+ }
+ if (scale_w) {
+ weights = ggml_scale(ctx, weights, w_scale);
+ cb(weights, "ffn_moe_weights_scaled", il);
+ }
+
+ cur = ggml_reshape_3d(ctx, cur, n_embd, 1, n_tokens);
+ ggml_tensor * up = llm_build_lora_mm_id(lctx, ctx, up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+ cb(up, "ffn_moe_up", il);
+
+ ggml_tensor * gate = llm_build_lora_mm_id(lctx, ctx, gate_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
+ cb(gate, "ffn_moe_gate", il);
+
+ switch (type_op) {
+ case LLM_FFN_SILU:
+ {
+ gate = ggml_silu(ctx, gate);
+ cb(gate, "ffn_moe_silu", il);
+ } break;
+ case LLM_FFN_GELU:
+ {
+ gate = ggml_gelu(ctx, gate);
+ cb(gate, "ffn_moe_gelu", il);
+ } break;
+ default:
+ GGML_ASSERT(false);
+ }
+
+ ggml_tensor * par = ggml_mul(ctx, up, gate); // [n_ff, n_expert_used, n_tokens]
+ cb(par, "ffn_moe_gate_par", il);
+
+ ggml_tensor * experts = llm_build_lora_mm_id(lctx, ctx, down_exps, par, selected_experts); // [n_embd, n_expert_used, n_tokens]
+ cb(experts, "ffn_moe_down", il);
+
+ experts = ggml_mul(ctx, experts, weights);
+
+ // aggregate experts
+ ggml_tensor * moe_out = nullptr;
+ for (int i = 0; i < n_expert_used; ++i) {
+ ggml_tensor * cur_expert = ggml_view_2d(ctx, experts, n_embd, n_tokens,
+ experts->nb[2], i*experts->nb[1]);
+
+ if (i == 0) {
+ moe_out = cur_expert;
+ } else {
+ moe_out = ggml_add(ctx, moe_out, cur_expert);
+ }
+ }
+
+ if (n_expert_used == 1) {
+ // avoid returning a non-contiguous tensor
+ moe_out = ggml_cont(ctx, moe_out);
+ }
+
+ return moe_out;
+}
+
+static struct ggml_tensor * llm_build_kqv(
+ struct ggml_context * ctx,
+ struct llama_context & lctx,
+ const llama_kv_cache & kv,
+ struct ggml_cgraph * graph,
+ struct ggml_tensor * wo,
+ struct ggml_tensor * wo_b,
+ struct ggml_tensor * q_cur,
+ struct ggml_tensor * kq_mask,
+ int32_t n_tokens,
+ int32_t n_kv,
+ float kq_scale,
+ const llm_build_cb & cb,
+ int il) {
+ const llama_model & model = lctx.model;
+ const llama_hparams & hparams = lctx.model.hparams;
+ const llama_cparams & cparams = lctx.cparams;
+
+ const int64_t n_ctx = cparams.n_ctx;
+ const int64_t n_head = hparams.n_head(il);
+ const int64_t n_head_kv = hparams.n_head_kv(il);
+ const int64_t n_embd_head_k = hparams.n_embd_head_k;
+ const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
+ const int64_t n_embd_head_v = hparams.n_embd_head_v;
+ const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
+
+ struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3);
+ cb(q, "q", il);
+
+ struct ggml_tensor * k =
+ ggml_view_3d(ctx, kv.k_l[il],
+ n_embd_head_k, n_kv, n_head_kv,
+ ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa),
+ ggml_row_size(kv.k_l[il]->type, n_embd_head_k),
+ 0);
+ cb(k, "k", il);
+
+ struct ggml_tensor * cur;
+
+ if (cparams.flash_attn) {
+ GGML_UNUSED(model);
+ GGML_UNUSED(n_ctx);
+
+ // split cached v into n_head heads (not transposed)
+ struct ggml_tensor * v =
+ ggml_view_3d(ctx, kv.v_l[il],
+ n_embd_head_v, n_kv, n_head_kv,
+ ggml_row_size(kv.v_l[il]->type, n_embd_v_gqa),
+ ggml_row_size(kv.v_l[il]->type, n_embd_head_v),
+ 0);
+ cb(v, "v", il);
+
+ cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias);
+
+ if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX) {
+ ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
+ }
+
+ cur = ggml_reshape_2d(ctx, cur, n_embd_head_v*n_head, n_tokens);
+ } else {
+ struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q);
+ cb(kq, "kq", il);
+
+ if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX || model.arch == LLM_ARCH_QWEN2) {
+ // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs
+ // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847
+ ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
+ }
+
+ if (model.arch == LLM_ARCH_GROK) {
+ // need to do the following:
+ // multiply by attn_output_multiplyer of 0.08838834764831845
+ // and then :
+ // kq = 30 * tanh(kq / 30)
+ // before the softmax below
+
+ //try from phi2
+ //ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
+
+ kq = ggml_tanh(ctx, ggml_scale(ctx, kq, 0.08838834764831845f/30.0f));
+ kq = ggml_scale(ctx, kq, 30);
+ }
+
+ if (hparams.attn_soft_cap) {
+ kq = ggml_scale(ctx, kq, 1.0f / hparams.f_attn_logit_softcapping);
+ kq = ggml_tanh(ctx, kq);
+ kq = ggml_scale(ctx, kq, hparams.f_attn_logit_softcapping);
+ }
+
+ kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale, hparams.f_max_alibi_bias);
+ cb(kq, "kq_soft_max_ext", il);
+
+ GGML_ASSERT(kv.size == n_ctx);
+
+ // split cached v into n_head heads
+ struct ggml_tensor * v =
+ ggml_view_3d(ctx, kv.v_l[il],
+ n_kv, n_embd_head_v, n_head_kv,
+ ggml_element_size(kv.v_l[il])*n_ctx,
+ ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head_v,
+ 0);
+ cb(v, "v", il);
+
+ struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq);
+ cb(kqv, "kqv", il);
+
+ struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3);
+ cb(kqv_merged, "kqv_merged", il);
+
+ cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_v*n_head, n_tokens);
+ cb(cur, "kqv_merged_cont", il);
+ }
+
+ ggml_build_forward_expand(graph, cur);
+
+ if (wo) {
+ cur = llm_build_lora_mm(lctx, ctx, wo, cur);
+ }
+
+ if (wo_b) {
+ cb(cur, "kqv_wo", il);
+ }
+
+ if (wo_b) {
+ cur = ggml_add(ctx, cur, wo_b);
+ }
+
+ return cur;
+}
+
+static struct ggml_tensor * llm_build_kv(
+ struct ggml_context * ctx,
+ struct llama_context & lctx,
+ const llama_kv_cache & kv,
+ struct ggml_cgraph * graph,
+ struct ggml_tensor * wo,
+ struct ggml_tensor * wo_b,
+ struct ggml_tensor * k_cur,
+ struct ggml_tensor * v_cur,
+ struct ggml_tensor * q_cur,
+ struct ggml_tensor * kq_mask,
+ int32_t n_tokens,
+ int32_t kv_head,
+ int32_t n_kv,
+ float kq_scale,
+ const llm_build_cb & cb,
+ int il) {
+ const llama_hparams & hparams = lctx.model.hparams;
+ const llama_cparams & cparams = lctx.cparams;
+
+ // these nodes are added to the graph together so that they are not reordered
+ // by doing so, the number of splits in the graph is reduced
+ ggml_build_forward_expand(graph, q_cur);
+ ggml_build_forward_expand(graph, k_cur);
+ ggml_build_forward_expand(graph, v_cur);
+
+ llm_build_kv_store(ctx, hparams, cparams, kv, graph, k_cur, v_cur, n_tokens, kv_head, cb, il);
+
+ struct ggml_tensor * cur;
+
+ cur = llm_build_kqv(ctx, lctx, kv, graph, wo, wo_b,
+ q_cur, kq_mask, n_tokens, n_kv, kq_scale, cb, il);
+ cb(cur, "kqv_out", il);
+
+ return cur;
+}
+
+struct llm_build_context {
+ const llama_model & model;
+ llama_context & lctx;
+ const llama_hparams & hparams;
+ const llama_cparams & cparams;
+ const llama_batch & batch;
+ const llama_kv_cache & kv_self;
+
+ const int64_t n_embd;
+ const int64_t n_layer;
+ const int64_t n_rot;
+ const int64_t n_ctx; // user-specified context size (can be different from n_ctx_train)
+ const int64_t n_head;
+ const int64_t n_head_kv;
+ const int64_t n_embd_head_k;
+ const int64_t n_embd_k_gqa;
+ const int64_t n_embd_head_v;
+ const int64_t n_embd_v_gqa;
+ const int64_t n_expert;
+ const int64_t n_expert_used;
+
+ const float freq_base;
+ const float freq_scale;
+ const float ext_factor;
+ const float attn_factor;
+ const float beta_fast;
+ const float beta_slow;
+ const float norm_eps;
+ const float norm_rms_eps;
+
+ const int32_t n_tokens;
+ const int32_t n_kv; // size of KV cache to consider (n_kv <= kv_self.size)
+ const int32_t n_outputs;
+ const int32_t n_outputs_enc;
+ const int32_t kv_head; // index of where we store new KV data in the cache
+ const int32_t n_ctx_orig;
+
+ const bool flash_attn;
+
+ const enum llama_pooling_type pooling_type;
+ const enum llama_rope_type rope_type;
+
+ const llm_build_cb & cb;
+
+ std::vector<uint8_t> & buf_compute_meta;
+
+ struct ggml_context * ctx0 = nullptr;
+
+ // TODO: consider making the entire interface noexcept
+ llm_build_context(
+ llama_context & lctx,
+ const llama_batch & batch,
+ const llm_build_cb & cb,
+ bool worst_case) :
+ model (lctx.model),
+ lctx (lctx),
+ hparams (model.hparams),
+ cparams (lctx.cparams),
+ batch (batch),
+ kv_self (lctx.kv_self),
+ n_embd (hparams.n_embd),
+ n_layer (hparams.n_layer),
+ n_rot (hparams.n_rot),
+ n_ctx (cparams.n_ctx),
+ n_head (hparams.n_head()),
+ n_head_kv (hparams.n_head_kv()),
+ n_embd_head_k (hparams.n_embd_head_k),
+ n_embd_k_gqa (hparams.n_embd_k_gqa()),
+ n_embd_head_v (hparams.n_embd_head_v),
+ n_embd_v_gqa (hparams.n_embd_v_gqa()),
+ n_expert (hparams.n_expert),
+ n_expert_used (hparams.n_expert_used),
+ freq_base (cparams.rope_freq_base),
+ freq_scale (cparams.rope_freq_scale),
+ ext_factor (cparams.yarn_ext_factor),
+ attn_factor (cparams.yarn_attn_factor),
+ beta_fast (cparams.yarn_beta_fast),
+ beta_slow (cparams.yarn_beta_slow),
+ norm_eps (hparams.f_norm_eps),
+ norm_rms_eps (hparams.f_norm_rms_eps),
+ n_tokens (batch.n_tokens),
+ n_kv (worst_case ? kv_self.size : kv_self.n),
+ n_outputs (worst_case ? n_tokens : lctx.n_outputs),
+ n_outputs_enc (worst_case ? n_tokens : lctx.embd_enc.size() / hparams.n_embd),
+ kv_head (worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head),
+ n_ctx_orig (cparams.n_ctx_orig_yarn),
+ flash_attn (cparams.flash_attn),
+ pooling_type (cparams.pooling_type),
+ rope_type (hparams.rope_type),
+ cb (cb),
+ buf_compute_meta (lctx.buf_compute_meta) {
+ // all initializations should be done in init()
+ }
+
+ void init() {
+ struct ggml_init_params params = {
+ /*.mem_size =*/ buf_compute_meta.size(),
+ /*.mem_buffer =*/ buf_compute_meta.data(),
+ /*.no_alloc =*/ true,
+ };
+
+ ctx0 = ggml_init(params);
+
+ lctx.inp_tokens = nullptr;
+ lctx.inp_embd = nullptr;
+ lctx.inp_pos = nullptr;
+ lctx.inp_out_ids = nullptr;
+ lctx.inp_KQ_mask = nullptr;
+ lctx.inp_KQ_mask_swa = nullptr;
+ lctx.inp_K_shift = nullptr;
+ lctx.inp_mean = nullptr;
+ lctx.inp_cls = nullptr;
+ lctx.inp_s_copy = nullptr;
+ lctx.inp_s_mask = nullptr;
+ lctx.inp_s_seq = nullptr;
+ lctx.inp_pos_bucket = nullptr;
+ lctx.inp_embd_enc = nullptr;
+ lctx.inp_KQ_mask_cross = nullptr;
+ }
+
+ void free() {
+ if (ctx0) {
+ ggml_free(ctx0);
+ ctx0 = nullptr;
+ }
+ }
+
+ struct ggml_cgraph * build_k_shift() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ GGML_ASSERT(kv_self.size == n_ctx);
+
+ lctx.inp_K_shift = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_ctx);
+ cb(lctx.inp_K_shift, "K_shift", -1);
+ ggml_set_input(lctx.inp_K_shift);
+
+ for (int il = 0; il < n_layer; ++il) {
+ const int64_t n_head_kv = hparams.n_head_kv(il);
+ const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
+ struct ggml_tensor * rope_factors = build_rope_factors(il);
+ struct ggml_tensor * tmp =
+ // we rotate only the first n_rot dimensions
+ ggml_rope_ext_inplace(ctx0,
+ ggml_view_3d(ctx0, kv_self.k_l[il],
+ n_embd_head_k, n_head_kv, n_ctx,
+ ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
+ ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
+ 0),
+ lctx.inp_K_shift, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+
+ cb(tmp, "K_shifted", il);
+ ggml_build_forward_expand(gf, tmp);
+ }
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_s_copy() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ GGML_ASSERT(kv_self.recurrent);
+
+ struct ggml_tensor * state_copy = build_inp_s_copy();
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size);
+ struct ggml_tensor * ssm_states = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size);
+
+ conv_states = ggml_get_rows(ctx0, conv_states, state_copy);
+ ssm_states = ggml_get_rows(ctx0, ssm_states, state_copy);
+
+ // TODO: name the intermediate tensors with cb()
+
+ ggml_build_forward_expand(gf, ggml_cpy(ctx0, conv_states, kv_self.k_l[il]));
+ ggml_build_forward_expand(gf, ggml_cpy(ctx0, ssm_states, kv_self.v_l[il]));
+ }
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_defrag(const std::vector<uint32_t> & ids) {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ for (uint32_t i = 0; i < ids.size(); ++i) {
+ const uint32_t id = ids[i];
+
+ if (i == id || id == ids.size()) {
+ continue;
+ }
+
+ uint32_t nm = 1;
+
+ while (i + nm < ids.size() && ids[i + nm] == id + nm) {
+ nm++;
+ }
+
+ for (int il = 0; il < n_layer; ++il) {
+ const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
+ const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
+
+ ggml_tensor * view_k_src = ggml_view_2d(ctx0, kv_self.k_l[il],
+ n_embd_k_gqa, nm,
+ ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
+ ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*i));
+
+ ggml_tensor * view_k_dst = ggml_view_2d(ctx0, kv_self.k_l[il],
+ n_embd_k_gqa, nm,
+ ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
+ ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*id));
+
+ ggml_tensor * view_v_src;
+ ggml_tensor * view_v_dst;
+
+ if (flash_attn) {
+ // NOTE: the V cache is not transposed when using flash attention
+ view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il],
+ n_embd_v_gqa, nm,
+ ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa),
+ ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*i));
+
+ view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il],
+ n_embd_v_gqa, nm,
+ ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa),
+ ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*id));
+ } else {
+ view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il],
+ nm, n_embd_v_gqa,
+ ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
+ ggml_row_size(kv_self.v_l[il]->type, i));
+
+ view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il],
+ nm, n_embd_v_gqa,
+ ggml_row_size(kv_self.v_l[il]->type, kv_self.size),
+ ggml_row_size(kv_self.v_l[il]->type, id));
+ }
+
+ ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_k_src, view_k_dst));
+ ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_v_src, view_v_dst));
+ }
+
+ i += nm - 1;
+ }
+
+ //LLAMA_LOG_INFO("gf->n_nodes = %d\n", gf->n_nodes);
+
+ return gf;
+ }
+
+ struct ggml_tensor * build_inp_pos() {
+ lctx.inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+ cb(lctx.inp_pos, "inp_pos", -1);
+ ggml_set_input(lctx.inp_pos);
+ return lctx.inp_pos;
+ }
+
+ struct ggml_tensor * build_rope_factors(int il) {
+ // choose long/short freq factors based on the context size
+ const auto n_ctx_pre_seq = cparams.n_ctx / cparams.n_seq_max;
+
+ if (n_ctx_pre_seq > hparams.n_ctx_orig_yarn) {
+ return model.layers[il].rope_long;
+ }
+
+ return model.layers[il].rope_short;
+ }
+
+ struct ggml_tensor * build_inp_out_ids() {
+ lctx.inp_out_ids = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_outputs);
+ cb(lctx.inp_out_ids, "inp_out_ids", -1);
+ ggml_set_input(lctx.inp_out_ids);
+ return lctx.inp_out_ids;
+ }
+
+ struct ggml_tensor * build_inp_KQ_mask(bool causal = true) {
+ lctx.inp_KQ_mask = causal
+ ? ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD))
+ : ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+ cb(lctx.inp_KQ_mask, "KQ_mask", -1);
+ ggml_set_input(lctx.inp_KQ_mask);
+
+ return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_mask, GGML_TYPE_F16) : lctx.inp_KQ_mask;
+ }
+
+ struct ggml_tensor * build_inp_KQ_mask_swa(bool causal = true) {
+ GGML_ASSERT(hparams.n_swa > 0);
+
+ lctx.inp_KQ_mask_swa = causal
+ ? ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD))
+ : ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+ cb(lctx.inp_KQ_mask_swa, "KQ_mask_swa", -1);
+ ggml_set_input(lctx.inp_KQ_mask_swa);
+
+ return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_mask_swa, GGML_TYPE_F16) : lctx.inp_KQ_mask_swa;
+ }
+
+ struct ggml_tensor * build_inp_mean() {
+ lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens);
+ cb(lctx.inp_mean, "inp_mean", -1);
+ ggml_set_input(lctx.inp_mean);
+ return lctx.inp_mean;
+ }
+
+ struct ggml_tensor * build_inp_cls() {
+ lctx.inp_cls = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+ cb(lctx.inp_cls, "inp_cls", -1);
+ ggml_set_input(lctx.inp_cls);
+ return lctx.inp_cls;
+ }
+
+ struct ggml_tensor * build_inp_s_copy() {
+ lctx.inp_s_copy = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, kv_self.size);
+ cb(lctx.inp_s_copy, "inp_s_copy", -1);
+ ggml_set_input(lctx.inp_s_copy);
+ return lctx.inp_s_copy;
+ }
+
+ struct ggml_tensor * build_inp_s_mask() {
+ lctx.inp_s_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_kv);
+ cb(lctx.inp_s_mask, "inp_s_mask", -1);
+ ggml_set_input(lctx.inp_s_mask);
+ return lctx.inp_s_mask;
+ }
+
+ struct ggml_tensor * build_inp_s_seq() {
+ lctx.inp_s_seq = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv, n_tokens);
+ cb(lctx.inp_s_seq, "inp_s_seq", -1);
+ ggml_set_input(lctx.inp_s_seq);
+ return lctx.inp_s_seq;
+ }
+
+ struct ggml_cgraph * append_pooling(struct ggml_cgraph * gf) {
+ // find result_norm tensor for input
+ struct ggml_tensor * inp = nullptr;
+ for (int i = gf->n_nodes - 1; i >= 0; --i) {
+ inp = gf->nodes[i];
+ if (strcmp(inp->name, "result_norm") == 0 || strcmp(inp->name, "result_embd") == 0) {
+ break;
+ } else {
+ inp = nullptr;
+ }
+ }
+ GGML_ASSERT(inp != nullptr && "missing result_norm/result_embd tensor");
+
+ struct ggml_tensor * cur;
+
+ switch (pooling_type) {
+ case LLAMA_POOLING_TYPE_MEAN:
+ {
+ struct ggml_tensor * inp_mean = build_inp_mean();
+ cur = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, inp)), inp_mean);
+ } break;
+ case LLAMA_POOLING_TYPE_CLS:
+ case LLAMA_POOLING_TYPE_LAST:
+ {
+ struct ggml_tensor * inp_cls = build_inp_cls();
+ cur = ggml_get_rows(ctx0, inp, inp_cls);
+ } break;
+ case LLAMA_POOLING_TYPE_NONE:
+ {
+ cur = inp;
+ } break;
+ default:
+ {
+ GGML_ASSERT(false && "unknown pooling type");
+ } break;
+ }
+
+ cb(cur, "result_embd_pooled", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_tensor * llm_build_pos_bucket(bool causal) {
+ if (causal) {
+ lctx.inp_pos_bucket = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv, n_tokens);
+ } else {
+ lctx.inp_pos_bucket = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_tokens, n_tokens);
+ }
+
+ ggml_set_input(lctx.inp_pos_bucket);
+ cb(lctx.inp_pos_bucket, "pos_bucket", -1);
+
+ return lctx.inp_pos_bucket;
+ }
+
+ struct ggml_tensor * llm_build_pos_bias(struct ggml_tensor * pos_bucket, struct ggml_tensor * attn_rel_b) {
+ struct ggml_tensor * pos_bucket_1d = ggml_view_1d(ctx0, pos_bucket, pos_bucket->ne[0] * pos_bucket->ne[1], 0);
+ cb(pos_bucket_1d, "pos_bucket_1d", -1);
+
+ struct ggml_tensor * pos_bias = ggml_get_rows(ctx0, attn_rel_b, pos_bucket_1d);
+ cb(pos_bias, "pos_bias", -1);
+
+ pos_bias = ggml_view_3d(ctx0, pos_bias, pos_bias->ne[0], lctx.inp_pos_bucket->ne[0], lctx.inp_pos_bucket->ne[1], ggml_element_size(pos_bias) * pos_bias->ne[0], ggml_element_size(pos_bias) * pos_bias->ne[0] * lctx.inp_pos_bucket->ne[0], 0);
+ cb(pos_bias, "pos_bias", -1);
+
+ pos_bias = ggml_permute(ctx0, pos_bias, 2, 0, 1, 3);
+ cb(pos_bias, "pos_bias", -1);
+
+ pos_bias = ggml_cont(ctx0, pos_bias);
+ cb(pos_bias, "pos_bias", -1);
+
+ return pos_bias;
+ }
+
+ struct ggml_tensor * llm_build_inp_embd_enc() {
+ const int64_t n_embd = hparams.n_embd;
+ lctx.inp_embd_enc = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_outputs_enc);
+ ggml_set_input(lctx.inp_embd_enc);
+ cb(lctx.inp_embd_enc, "embd_enc", -1);
+ return lctx.inp_embd_enc;
+ }
+
+ struct ggml_tensor * llm_build_inp_KQ_mask_cross() {
+ lctx.inp_KQ_mask_cross = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_outputs_enc, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+ ggml_set_input(lctx.inp_KQ_mask_cross);
+ cb(lctx.inp_KQ_mask_cross, "KQ_mask_cross", -1);
+ return lctx.inp_KQ_mask_cross;
+ }
+
+ struct ggml_cgraph * build_llama() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ // mutable variable, needed during the last layer of the computation to skip unused tokens
+ int32_t n_tokens = this->n_tokens;
+
+ 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, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ 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, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ n_tokens = n_outputs;
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network
+ if (model.layers[il].ffn_gate_inp == nullptr) {
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+ model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+ } else {
+ // MoE branch
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_moe_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_gate_inp,
+ model.layers[il].ffn_up_exps,
+ model.layers[il].ffn_gate_exps,
+ model.layers[il].ffn_down_exps,
+ n_expert, n_expert_used,
+ LLM_FFN_SILU, true,
+ false, 0.0,
+ cb, il);
+ cb(cur, "ffn_moe_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cb(cur, "ffn_out", il);
+
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_baichuan() {
+ 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, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = model.type == MODEL_7B ? build_inp_pos() : nullptr;
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * inpSA = inpL;
+
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+
+ struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+
+ switch (model.type) {
+ case MODEL_7B:
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ break;
+ case MODEL_13B:
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd/n_head, n_head, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd/n_head, n_head, n_tokens);
+ break;
+ default:
+ GGML_ASSERT(false);
+ }
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, NULL,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ 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, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_xverse() {
+ 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, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * inpSA = inpL;
+
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+
+ struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, NULL,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ 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, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams, model.output_norm, NULL, LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_falcon() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+ 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, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * attn_norm;
+
+ attn_norm = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm,
+ model.layers[il].attn_norm_b,
+ LLM_NORM, cb, il);
+ cb(attn_norm, "attn_norm", il);
+
+ // self-attention
+ {
+ if (model.layers[il].attn_norm_2) {
+ // Falcon-40B
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm_2,
+ model.layers[il].attn_norm_2_b,
+ LLM_NORM, cb, il);
+ cb(cur, "attn_norm_2", il);
+ } else {
+ cur = attn_norm;
+ }
+
+ cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+ cb(cur, "wqkv", il);
+
+ struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+ struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+ struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+
+ // using mode = 2 for neox mode
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
+ freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
+ freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, NULL,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ attn_norm = ggml_get_rows(ctx0, attn_norm, inp_out_ids);
+ }
+
+ struct ggml_tensor * ffn_inp = cur;
+
+ // feed forward
+ {
+ cur = llm_build_ffn(ctx0, lctx, attn_norm, // !! use the attn norm, not the result
+ model.layers[il].ffn_up, NULL, NULL,
+ NULL, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = ggml_add(ctx0, cur, inpL);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ // norm
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm,
+ model.output_norm_b,
+ LLM_NORM, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_grok() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ // mutable variable, needed during the last layer of the computation to skip unused tokens
+ int32_t n_tokens = this->n_tokens;
+
+ 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, lctx, hparams, batch, model.tok_embd, cb);
+
+ // multiply by embedding_multiplier_scale of 78.38367176906169
+ inpL = ggml_scale(ctx0, inpL, 78.38367176906169f);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ 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, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+
+ // self-attention
+ {
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ n_tokens = n_outputs;
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ // Grok
+ // if attn_out_norm is present then apply it before adding the input
+ if (model.layers[il].attn_out_norm) {
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.layers[il].attn_out_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_out_norm", il);
+ }
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network
+ // MoE branch
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_moe_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_gate_inp,
+ model.layers[il].ffn_up_exps,
+ model.layers[il].ffn_gate_exps,
+ model.layers[il].ffn_down_exps,
+ n_expert, n_expert_used,
+ LLM_FFN_GELU, true,
+ false, 0.0,
+ cb, il);
+ cb(cur, "ffn_moe_out", il);
+
+ // Grok
+ // if layer_out_norm is present then apply it before adding the input
+ // Idea: maybe ffn_out_norm is a better name
+ if (model.layers[il].layer_out_norm) {
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.layers[il].layer_out_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "layer_out_norm", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cb(cur, "ffn_out", il);
+
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+
+ // Grok
+ // multiply logits by output_multiplier_scale of 0.5773502691896257
+
+ cur = ggml_scale(ctx0, cur, 0.5773502691896257f);
+
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_dbrx() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ // mutable variable, needed during the last layer of the computation to skip unused tokens
+ int32_t n_tokens = this->n_tokens;
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+ 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, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ 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, NULL,
+ LLM_NORM, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ struct ggml_tensor * Qcur = nullptr;
+ struct ggml_tensor * Kcur = nullptr;
+ struct ggml_tensor * Vcur = nullptr;
+
+ cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+ cb(cur, "wqkv", il);
+
+ cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+ cb(cur, "wqkv_clamped", il);
+
+ Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+ Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+ Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, NULL,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ n_tokens = n_outputs;
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network
+ // MoE branch
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].attn_out_norm, NULL,
+ LLM_NORM, cb, il);
+ cb(cur, "attn_out_norm", il);
+
+ cur = llm_build_moe_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_gate_inp,
+ model.layers[il].ffn_up_exps,
+ model.layers[il].ffn_gate_exps,
+ model.layers[il].ffn_down_exps,
+ n_expert, n_expert_used,
+ LLM_FFN_SILU, true,
+ false, 0.0,
+ cb, il);
+ cb(cur, "ffn_moe_out", il);
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cb(cur, "ffn_out", il);
+
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_starcoder() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ struct ggml_tensor * pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
+ cb(pos, "pos_embd", -1);
+
+ inpL = ggml_add(ctx0, inpL, pos);
+ cb(inpL, "inpL", -1);
+
+ for (int il = 0; il < n_layer; ++il) {
+ 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
+ {
+ cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+ cb(cur, "wqkv", il);
+
+ cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+ cb(cur, "bqkv", il);
+
+ struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+ struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+ struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ }
+
+ // add the input
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // FF
+ {
+ 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, lctx, cur,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+ NULL, NULL, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ NULL,
+ LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.output_norm,
+ model.output_norm_b,
+ LLM_NORM, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_refact() {
+ 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);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * inpSA = inpL;
+
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+
+ struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+ cb(Kcur, "Kcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ cb(Qcur, "Qcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, NULL,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ 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, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_bert() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+ struct ggml_tensor * inp_pos = nullptr;
+
+ if (model.arch != LLM_ARCH_JINA_BERT_V2) {
+ inp_pos = build_inp_pos();
+ }
+
+ // construct input embeddings (token, type, position)
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // token types are hardcoded to zero ("Sentence A")
+ struct ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0);
+ inpL = ggml_add(ctx0, inpL, type_row0);
+ if (model.arch == LLM_ARCH_BERT) {
+ inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL);
+ }
+ cb(inpL, "inp_embd", -1);
+
+ // embed layer norm
+ inpL = llm_build_norm(ctx0, inpL, hparams, model.tok_norm, model.tok_norm_b, LLM_NORM, cb, -1);
+ cb(inpL, "inp_norm", -1);
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask(false);
+
+ // iterate layers
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * cur = inpL;
+
+ struct ggml_tensor * Qcur;
+ struct ggml_tensor * Kcur;
+ struct ggml_tensor * Vcur;
+
+ // self-attention
+ if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_JINA_BERT_V2) {
+ Qcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur), model.layers[il].bq);
+ cb(Qcur, "Qcur", il);
+
+ if (model.layers[il].attn_q_norm) {
+ Qcur = llm_build_norm(ctx0, Qcur, hparams,
+ model.layers[il].attn_q_norm,
+ model.layers[il].attn_q_norm_b,
+ LLM_NORM, cb, il);
+ }
+
+ Kcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur), model.layers[il].bk);
+ cb(Kcur, "Kcur", il);
+
+ if (model.layers[il].attn_k_norm) {
+ Kcur = llm_build_norm(ctx0, Kcur, hparams,
+ model.layers[il].attn_k_norm,
+ model.layers[il].attn_k_norm_b,
+ LLM_NORM, cb, il);
+ }
+ Vcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur), model.layers[il].bv);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+ } else {
+ // compute Q and K and RoPE them
+ cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+ cb(cur, "wqkv", il);
+
+ Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+ Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+ Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+ }
+
+ struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+ struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
+
+ struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+ cb(kq, "kq", il);
+
+ kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, 1.0f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias);
+ cb(kq, "kq_soft_max_ext", il);
+
+ struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens)));
+ cb(v, "v", il);
+
+ struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_tokens, n_embd_head, n_head_kv), kq);
+ cb(kqv, "kqv", il);
+
+ struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+ cb(kqv_merged, "kqv_merged", il);
+
+ cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
+ cb(cur, "kqv_merged_cont", il);
+
+ ggml_build_forward_expand(gf, cur);
+
+ cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wo, cur);
+ if (model.layers[il].bo) {
+ cb(cur, "kqv_wo", il);
+ }
+
+ if (model.layers[il].bo) {
+ cur = ggml_add(ctx0, cur, model.layers[il].bo);
+ }
+ cb(cur, "kqv_out", il);
+
+ if (il == n_layer - 1 && pooling_type == LLAMA_POOLING_TYPE_NONE) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ }
+
+ // re-add the layer input
+ cur = ggml_add(ctx0, cur, inpL);
+
+ // attention layer norm
+ cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_out_norm, model.layers[il].attn_out_norm_b, LLM_NORM, cb, il);
+
+ if (model.layers[il].attn_norm_2 != nullptr) {
+ cur = ggml_add(ctx0, cur, inpL); // re-add the layer input
+ cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_norm_2, model.layers[il].attn_norm_2_b, LLM_NORM, cb, il);
+ }
+
+ struct ggml_tensor * ffn_inp = cur;
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network
+ if (model.arch == LLM_ARCH_BERT) {
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+ NULL, NULL, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ NULL,
+ LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+ } else if (model.arch == LLM_ARCH_JINA_BERT_V2) {
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ NULL,
+ LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
+ } else {
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ }
+ cb(cur, "ffn_out", il);
+
+ // attentions bypass the intermediate layer
+ cur = ggml_add(ctx0, cur, ffn_inp);
+
+ // output layer norm
+ cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].layer_out_norm, model.layers[il].layer_out_norm_b, LLM_NORM, cb, il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ // final output
+ cur = inpL;
+ cb(cur, "result_embd", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_bloom() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ inpL = llm_build_norm(ctx0, inpL, hparams,
+ model.tok_norm,
+ model.tok_norm_b,
+ LLM_NORM, cb, -1);
+ cb(inpL, "inp_norm", -1);
+
+ for (int il = 0; il < n_layer; ++il) {
+ 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
+ {
+ cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+ cb(cur, "wqkv", il);
+
+ cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+ cb(cur, "bqkv", il);
+
+ struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+ struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+ struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ }
+
+ // Add the input
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // FF
+ {
+ 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, lctx, cur,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+ NULL, NULL, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ NULL,
+ LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.output_norm,
+ model.output_norm_b,
+ LLM_NORM, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_mpt() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * pos;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ if (model.pos_embd) {
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+ pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
+ cb(pos, "pos_embd", -1);
+
+ inpL = ggml_add(ctx0, inpL, pos);
+ cb(inpL, "inpL", -1);
+ }
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * attn_norm;
+
+ attn_norm = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm,
+ model.layers[il].attn_norm_b,
+ LLM_NORM, cb, il);
+ cb(attn_norm, "attn_norm", il);
+
+ // self-attention
+ {
+ cur = attn_norm;
+
+ cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+ cb(cur, "wqkv", il);
+
+ if (model.layers[il].bqkv){
+ cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+ cb(cur, "bqkv", il);
+ }
+
+ if (hparams.f_clamp_kqv > 0.0f) {
+ cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+ cb(cur, "wqkv_clamped", il);
+ }
+
+ struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+ struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+ struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ // Q/K Layernorm
+ if (model.layers[il].attn_q_norm) {
+ Qcur = llm_build_norm(ctx0, Qcur, hparams,
+ model.layers[il].attn_q_norm,
+ model.layers[il].attn_q_norm_b,
+ LLM_NORM, cb, il);
+ cb(Qcur, "Qcur", il);
+
+ Kcur = llm_build_norm(ctx0, Kcur, hparams,
+ model.layers[il].attn_k_norm,
+ model.layers[il].attn_k_norm_b,
+ LLM_NORM, cb, il);
+ cb(Kcur, "Kcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ } else {
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ }
+
+ // Add the input
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed forward
+ {
+ 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, lctx, cur,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+ NULL, NULL, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ model.layers[il].ffn_act,
+ LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ 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);
+
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_stablelm() {
+ struct ggml_cgraph * gf = ggml_new_graph(ctx0);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+
+
+ // 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);
+
+ struct ggml_tensor * inpSA = cur;
+
+ // self-attention
+ {
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ cb(Qcur, "Qcur", il);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+ cb(Kcur, "Kcur", il);
+
+ if (model.layers[il].attn_q_norm) {
+ Qcur = llm_build_norm(ctx0, Qcur, hparams,
+ model.layers[il].attn_q_norm,
+ NULL,
+ LLM_NORM, cb, il);
+ cb(Qcur, "Qcur", il);
+ }
+ if (model.layers[il].attn_k_norm) {
+ Kcur = llm_build_norm(ctx0, Kcur, hparams,
+ model.layers[il].attn_k_norm,
+ NULL,
+ LLM_NORM, cb, il);
+ cb(Kcur, "Kcur", il);
+ }
+
+
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, NULL,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network
+ {
+ if (model.layers[il].ffn_norm) {
+ 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);
+ } else {
+ // parallel residual
+ cur = inpSA;
+ }
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ 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 = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_qwen() {
+ 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);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * inpSA = inpL;
+
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+ cb(cur, "wqkv", il);
+
+ cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+ cb(cur, "bqkv", il);
+
+ struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+ struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+ struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 2*sizeof(float)*(n_embd)));
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+
+ // using mode = 2 for neox mode
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
+ freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
+ freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, NULL,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward forward
+ {
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_qwen2() {
+ 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, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ 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, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+ Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+ Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+ cb(Kcur, "Kcur", il);
+
+ struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+ Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ 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, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_qwen2moe() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ // mutable variable, needed during the last layer of the computation to skip unused tokens
+ int32_t n_tokens = this->n_tokens;
+
+ 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, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ 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, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self_attention
+ {
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+ Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+ Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+ cb(Kcur, "Kcur", il);
+
+ struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+ Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ n_tokens = n_outputs;
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // MoE branch
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ ggml_tensor * moe_out =
+ llm_build_moe_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_gate_inp,
+ model.layers[il].ffn_up_exps,
+ model.layers[il].ffn_gate_exps,
+ model.layers[il].ffn_down_exps,
+ n_expert, n_expert_used,
+ LLM_FFN_SILU, false,
+ false, 0.0,
+ cb, il);
+ cb(cur, "ffn_moe_out", il);
+
+ // FFN shared expert
+ {
+ ggml_tensor * cur_gate_inp = llm_build_lora_mm(lctx, ctx0, model.layers[il].ffn_gate_inp_shexp, cur);
+ cb(cur_gate_inp, "ffn_shexp_gate_inp", il);
+
+ // sigmoid
+ ggml_tensor * cur_gate = ggml_div(ctx0, ggml_silu(ctx0, cur_gate_inp), cur_gate_inp);
+ cb(cur_gate, "ffn_shexp_gate", il);
+
+ ggml_tensor * cur_ffn = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up_shexp, NULL, NULL,
+ model.layers[il].ffn_gate_shexp, NULL, NULL,
+ model.layers[il].ffn_down_shexp, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur_ffn, "ffn_shexp", il);
+
+ ggml_tensor * ffn_shexp_out = ggml_mul(ctx0, cur_ffn, cur_gate);
+ cb(ffn_shexp_out, "ffn_shexp_out", il);
+
+ moe_out = ggml_add(ctx0, moe_out, ffn_shexp_out);
+ cb(moe_out, "ffn_out", il);
+
+ cur = moe_out;
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_phi2() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * attn_norm_output;
+ struct ggml_tensor * ffn_output;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+ attn_norm_output = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm,
+ model.layers[il].attn_norm_b,
+ LLM_NORM, cb, il);
+ cb(attn_norm_output, "attn_norm", il);
+
+ // self-attention
+ {
+ struct ggml_tensor * Qcur = nullptr;
+ struct ggml_tensor * Kcur = nullptr;
+ struct ggml_tensor * Vcur = nullptr;
+
+ if (model.layers[il].wqkv) {
+ cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, attn_norm_output);
+ cb(cur, "wqkv", il);
+
+ cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+ cb(cur, "bqkv", il);
+
+ Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+ Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+ Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+ } else {
+ Qcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
+ Kcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
+ Vcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
+ }
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
+ freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ // with phi2, we scale the Q to avoid precision issues
+ // ref: https://github.com/ml-explore/mlx-examples/blob/08e862336ade809bc37d1035f94b359e7d1a5152/phi2/phi2.py#L64-L66
+ Qcur = ggml_scale(ctx0, Qcur, 1.0f/sqrtf(float(n_embd_head)));
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
+ freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ attn_norm_output = ggml_get_rows(ctx0, attn_norm_output, inp_out_ids);
+ }
+
+ // FF
+ {
+ ffn_output = llm_build_ffn(ctx0, lctx, attn_norm_output,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+ NULL, NULL, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ NULL,
+ LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+ cb(ffn_output, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_output);
+ cur = ggml_add(ctx0, cur, inpL);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.output_norm,
+ model.output_norm_b,
+ LLM_NORM, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output_no_bias", -1);
+
+ cur = ggml_add(ctx0, cur, model.output_b);
+ cb(cur, "result_output", -1);
+ ggml_build_forward_expand(gf, cur);
+ return gf;
+ }
+
+ struct ggml_cgraph * build_phi3() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa();
+
+ for (int il = 0; il < n_layer; ++il) {
+ auto residual = inpL;
+
+ // self-attention
+ {
+ // rope freq factors for 128k context
+ struct ggml_tensor * rope_factors = build_rope_factors(il);
+
+ struct ggml_tensor* attn_norm_output = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm,
+ NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(attn_norm_output, "attn_norm", il);
+
+ struct ggml_tensor * Qcur = nullptr;
+ struct ggml_tensor * Kcur = nullptr;
+ struct ggml_tensor * Vcur = nullptr;
+
+ if (model.layers[il].wqkv) {
+ cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, attn_norm_output);
+ cb(cur, "wqkv", il);
+
+ Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0 * sizeof(float) * (n_embd)));
+ Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd)));
+ Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa)));
+ }
+ else {
+ Qcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, attn_norm_output), model.layers[il].bq);
+ Kcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, attn_norm_output), model.layers[il].bk);
+ Vcur = ggml_add(ctx0, llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, attn_norm_output), model.layers[il].bv);
+ }
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig,
+ freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head)));
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig,
+ freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask_swa, n_tokens, kv_head, n_kv, 1.0f, cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor* inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ residual = ggml_get_rows(ctx0, residual, inp_out_ids);
+ }
+
+ cur = ggml_add(ctx0, cur, residual);
+ residual = cur;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ // FF
+ // special-case: the up and gate tensors are merged into a single tensor
+ // TOOD: support into llm_build_ffn
+ {
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ NULL, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SWIGLU, LLM_FFN_SEQ, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, residual, cur);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.output_norm,
+ NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+
+ struct ggml_cgraph * build_plamo() {
+ struct ggml_cgraph * gf = ggml_new_graph(ctx0);
+
+ 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, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+
+ // norm
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ struct ggml_tensor * attention_norm = cur;
+
+ // self-attention
+ {
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+
+ struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head, n_tokens), inp_pos, nullptr,
+ n_embd_head, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_embd_head, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, NULL,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+ struct ggml_tensor * sa_out = cur;
+
+ cur = attention_norm;
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ sa_out = ggml_get_rows(ctx0, sa_out, inp_out_ids);
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ }
+
+ // feed-forward network
+ {
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, sa_out);
+ cur = ggml_add(ctx0, cur, inpL);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_gpt2() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * pos;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
+ cb(pos, "pos_embd", -1);
+
+ inpL = ggml_add(ctx0, inpL, pos);
+ cb(inpL, "inpL", -1);
+
+ for (int il = 0; il < n_layer; ++il) {
+ 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
+ {
+ cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+ cb(cur, "wqkv", il);
+
+ cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+ cb(cur, "bqkv", il);
+
+ struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+ struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+ struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ }
+
+ // add the input
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // FF
+ {
+ 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, lctx, cur,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+ NULL, NULL, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ NULL,
+ LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.output_norm,
+ model.output_norm_b,
+ LLM_NORM, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_codeshell() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+ 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, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+ 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
+ {
+ cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+ cb(cur, "wqkv", il);
+
+ cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+ cb(cur, "bqkv", il);
+
+ struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+ struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+ struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+ cb(tmpq, "tmpq", il);
+ cb(tmpk, "tmpk", il);
+ cb(Vcur, "Vcur", il);
+
+ struct ggml_tensor * Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ }
+
+ // add the input
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // FF
+ {
+ 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, lctx, cur,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+ NULL, NULL, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ NULL,
+ LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.output_norm,
+ model.output_norm_b,
+ LLM_NORM, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ 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, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ 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 = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, NULL,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ 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, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ 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 = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_internlm2() {
+ 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, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ 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, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ 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, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ // ref: https://arxiv.org/abs/2203.03466
+ // https://github.com/ggerganov/llama.cpp/issues/5276#issuecomment-1925774738
+ // based on the original build_llama() function
+ struct ggml_cgraph * build_minicpm() {
+ 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);
+
+ const int64_t n_embd = hparams.n_embd;
+ //TODO: if the model varies, these parameters need to be read from the model
+ const int64_t n_embd_base = 256;
+ const float scale_embd = 12.0f;
+ const float scale_depth = 1.4f;
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // scale the input embeddings
+ inpL = ggml_scale(ctx0, inpL, scale_embd);
+ cb(inpL, "inp_scaled", -1);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ 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, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ // scale_res - scale the hidden states for residual connection
+ const float scale_res = scale_depth/sqrtf(float(n_layer));
+ cur = ggml_scale(ctx0, cur, scale_res);
+ cb(cur, "hidden_scaled", -1);
+
+ 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, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ // scale the hidden states for residual connection
+ cur = ggml_scale(ctx0, cur, scale_res);
+ cb(cur, "hidden_scaled_ffn", -1);
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head scaling
+ const float scale_lmhead = float(n_embd_base)/float(n_embd);
+ cur = ggml_scale(ctx0, cur, scale_lmhead);
+ cb(cur, "lmhead_scaling", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_gemma() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t n_embd_head_k = hparams.n_embd_head_k;
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
+ cb(inpL, "inp_scaled", -1);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+ // norm
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+
+ struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+ cb(Qcur, "Qcur", il);
+
+ Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k)));
+ cb(Qcur, "Qcur_scaled", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, NULL,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ }
+
+ struct ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
+ cb(sa_out, "sa_out", il);
+
+ cur = llm_build_norm(ctx0, sa_out, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ // feed-forward network
+ {
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, sa_out);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_gemma2() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t n_embd_head_k = hparams.n_embd_head_k;
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
+ cb(inpL, "inp_scaled", -1);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ // gemma 2 requires different mask for layers using sliding window (SWA)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask(true);
+ struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa(true);
+
+ for (int il = 0; il < n_layer; ++il) {
+ // (il % 2) layers use SWA
+ struct ggml_tensor * KQ_mask_l = (il % 2 == 0) ? KQ_mask_swa : KQ_mask;
+
+ // norm
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+
+ struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+ cb(Qcur, "Qcur", il);
+
+ // ref: https://github.com/google/gemma_pytorch/commit/03e657582d17cb5a8617ebf333c1c16f3694670e
+ switch (model.type) {
+ case e_model::MODEL_9B: Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k))); break;
+ case e_model::MODEL_27B: Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd / n_head))); break;
+ default: GGML_ASSERT(false);
+ };
+ cb(Qcur, "Qcur_scaled", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow);
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, NULL,
+ Kcur, Vcur, Qcur, KQ_mask_l, n_tokens, kv_head, n_kv, 1.0f, cb, il);
+ }
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.layers[il].attn_post_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_post_norm", il);
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ }
+
+ struct ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
+ cb(sa_out, "sa_out", il);
+
+ cur = llm_build_norm(ctx0, sa_out, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ // feed-forward network
+ {
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.layers[il].ffn_post_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "ffn_post_norm", -1);
+
+ cur = ggml_add(ctx0, cur, sa_out);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+
+ // final logit soft-capping
+ cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);
+ cur = ggml_tanh(ctx0, cur);
+ cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping);
+
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+
+ struct ggml_cgraph * build_starcoder2() {
+ 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, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ 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 = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ 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, lctx, cur,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+ NULL, NULL, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ NULL,
+ LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+ cb(cur, "ffn_out", il);
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ 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 = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_mamba() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t d_model = n_embd;
+ const int64_t d_conv = hparams.ssm_d_conv;
+ const int64_t d_inner = hparams.ssm_d_inner;
+ GGML_ASSERT(2 * d_model == d_inner);
+ const int64_t d_state = hparams.ssm_d_state;
+ const int64_t dt_rank = hparams.ssm_dt_rank;
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ // {n_embd, n_tokens}
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ struct ggml_tensor * state_mask = build_inp_s_mask();
+ struct ggml_tensor * state_seq = build_inp_s_seq();
+
+ for (int il = 0; il < n_layer; ++il) {
+ // (ab)using the KV cache to store the states
+ struct ggml_tensor * conv_states = ggml_reshape_2d(ctx0, kv_self.k_l[il], hparams.n_embd_k_s(), kv_self.size);
+ struct ggml_tensor * ssm_states = ggml_reshape_2d(ctx0, kv_self.v_l[il], hparams.n_embd_v_s(), kv_self.size);
+
+ // clear states of sequences which are starting at the beginning of this batch
+ {
+ conv_states = ggml_mul(ctx0,
+ ggml_view_2d(ctx0, conv_states, conv_states->ne[0], n_kv, conv_states->nb[1], kv_head*conv_states->nb[1]),
+ state_mask);
+ ssm_states = ggml_mul(ctx0,
+ ggml_view_2d(ctx0, ssm_states, ssm_states->ne[0], n_kv, ssm_states->nb[1], kv_head*ssm_states->nb[1]),
+ state_mask);
+ }
+
+ conv_states = ggml_reshape_3d(ctx0, conv_states, d_conv - 1, d_inner, n_kv);
+ ssm_states = ggml_reshape_3d(ctx0, ssm_states, d_state, d_inner, n_kv);
+
+ // norm
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // {n_embd, 2*d_inner} * {n_embd, n_tokens} => {2*d_inner, n_tokens}
+ struct ggml_tensor * xz = llm_build_lora_mm(lctx, ctx0, model.layers[il].ssm_in, cur);
+ // split the above in two
+ // => {d_inner, n_tokens}
+ struct ggml_tensor * x = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], 0);
+ struct ggml_tensor * z = ggml_view_2d(ctx0, xz, d_inner, xz->ne[1], xz->nb[1], ggml_element_size(xz)*d_inner);
+
+ // conv
+ {
+ // Custom operator which is needed only to ease simultaneous sequence processing.
+ // For a single sequence, the equivalent is to concatenate the columns of conv_states and x,
+ // then make a self-overlapping view of that over d_conv columns at each stride in the 3rd dimension,
+ // then element-wise multiply that with the conv1d weigth,
+ // then sum the elements of each row,
+ // (the last two steps are a dot product over rows (also doable with mul_mat))
+ // then permute away the ne[0] dimension,
+ // and then you're left with the resulting x tensor.
+ // The new conv_states is the last (d_conv - 1) columns
+ // of the last 3rd dimensional "layer" of the self-overlapping view.
+ // For simultaneous sequences, it's more complicated.
+ struct ggml_tensor * x_conv = ggml_ssm_conv(ctx0, conv_states, x, model.layers[il].ssm_conv1d, state_seq);
+
+ // store last (d_conv - 1) columns of the conv_state part of x_conv back into the KV cache
+ ggml_build_forward_expand(gf,
+ ggml_cpy(ctx0,
+ ggml_view_2d(ctx0, x_conv, d_conv - 1, d_inner*n_kv, d_conv*ggml_element_size(x_conv), (1+d_inner*n_tokens)*ggml_element_size(x_conv)),
+ ggml_view_1d(ctx0, kv_self.k_l[il], (d_conv - 1)*(d_inner)*(n_kv), kv_head*(d_conv - 1)*(d_inner)*ggml_element_size(x_conv))));
+
+ // extract x from x_conv
+ x = ggml_view_2d(ctx0, x_conv, d_inner, n_tokens, d_inner*ggml_element_size(x_conv), 0);
+
+ // bias
+ x = ggml_add(ctx0, x, model.layers[il].ssm_conv1d_b);
+
+ x = ggml_silu(ctx0, x);
+ }
+
+ // ssm
+ {
+ // {d_inner, dt_rank + 2*d_state} * {d_inner, n_tokens} => {dt_rank + 2*d_state, n_tokens}
+ struct ggml_tensor * x_db = llm_build_lora_mm(lctx, ctx0, model.layers[il].ssm_x, x);
+ // split
+ struct ggml_tensor * dt = ggml_view_2d(ctx0, x_db, dt_rank, n_tokens, x_db->nb[1], 0);
+ struct ggml_tensor * B = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*dt_rank);
+ struct ggml_tensor * C = ggml_view_2d(ctx0, x_db, d_state, n_tokens, x_db->nb[1], ggml_element_size(x_db)*(dt_rank+d_state));
+
+ // {dt_rank, d_inner} * {dt_rank, n_tokens} => {d_inner, n_tokens}
+ dt = llm_build_lora_mm(lctx, ctx0, model.layers[il].ssm_dt, dt);
+ dt = ggml_add(ctx0, dt, model.layers[il].ssm_dt_b);
+
+ // Custom operator to optimize the parallel associative scan
+ // as described in the Annex D of the Mamba paper.
+ // => {d_inner, n_tokens} and {d_state, d_inner, n_kv} combined,
+ // because only a single tensor can be returned.
+ struct ggml_tensor * y_ssm_states = ggml_ssm_scan(ctx0, ssm_states, x, dt, model.layers[il].ssm_a, B, C, state_seq);
+
+ // store last states (the second part of y_ssm_states)
+ ggml_build_forward_expand(gf,
+ ggml_cpy(ctx0,
+ ggml_view_1d(ctx0, y_ssm_states, d_state*d_inner*n_kv, d_inner*n_tokens*ggml_element_size(y_ssm_states)),
+ ggml_view_1d(ctx0, kv_self.v_l[il], d_state*d_inner*n_kv, kv_head*d_state*d_inner*ggml_element_size(ssm_states))));
+
+ struct ggml_tensor * y = ggml_view_2d(ctx0, y_ssm_states, d_inner, n_tokens, d_inner*ggml_element_size(y_ssm_states), 0);
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ x = ggml_get_rows(ctx0, x, inp_out_ids);
+ y = ggml_get_rows(ctx0, y, inp_out_ids);
+ z = ggml_get_rows(ctx0, z, inp_out_ids);
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ }
+
+ // {d_inner, n_tokens} * {d_inner} => {d_inner, n_tokens}
+ y = ggml_add(ctx0, y, ggml_mul(ctx0, x, model.layers[il].ssm_d));
+ y = ggml_mul(ctx0, y, ggml_silu(ctx0, z));
+
+ // {d_inner, n_embd} * {d_inner, n_tokens} => {n_embd, n_tokens}
+ cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].ssm_out, y);
+ }
+
+ // residual
+ cur = ggml_add(ctx0, cur, inpL);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ // final rmsnorm
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_command_r() {
+
+ 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);
+ const float f_logit_scale = hparams.f_logit_scale;
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+
+ // norm
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM, cb, il);
+ cb(cur, "attn_norm", il);
+ struct ggml_tensor * ffn_inp = cur;
+
+ // self-attention
+ {
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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 = llm_build_lora_mm(lctx, 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);
+ }
+
+ if (model.layers[il].attn_q_norm) {
+ Qcur = ggml_view_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens,
+ ggml_element_size(Qcur) * n_embd_head,
+ ggml_element_size(Qcur) * n_embd_head * n_head,
+ 0);
+ cb(Qcur, "Qcur", il);
+ Kcur = ggml_view_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens,
+ ggml_element_size(Kcur) * n_embd_head,
+ ggml_element_size(Kcur) * n_embd_head * n_head_kv,
+ 0);
+ cb(Kcur, "Kcur", il);
+
+ Qcur = llm_build_norm(ctx0, Qcur, hparams,
+ model.layers[il].attn_q_norm,
+ NULL,
+ LLM_NORM, cb, il);
+ cb(Qcur, "Qcur", il);
+
+ Kcur = llm_build_norm(ctx0, Kcur, hparams,
+ model.layers[il].attn_k_norm,
+ NULL,
+ LLM_NORM, cb, il);
+ cb(Kcur, "Kcur", il);
+ }
+
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
+ }
+
+ struct ggml_tensor * attn_out = cur;
+
+ // feed-forward network
+ {
+ cur = llm_build_ffn(ctx0, lctx, ffn_inp,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ // add together residual + FFN + self-attention
+ cur = ggml_add(ctx0, cur, inpL);
+ cur = ggml_add(ctx0, cur, attn_out);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+
+ if (f_logit_scale) {
+ cur = ggml_scale(ctx0, cur, f_logit_scale);
+ }
+
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+
+ }
+
+ // ref: https://allenai.org/olmo
+ // based on the original build_llama() function, changes:
+ // * non-parametric layer norm
+ // * clamp qkv
+ // * removed bias
+ // * removed MoE
+ struct ggml_cgraph * build_olmo() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ // mutable variable, needed during the last layer of the computation to skip unused tokens
+ int32_t n_tokens = this->n_tokens;
+
+ 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, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * inpSA = inpL;
+
+ // norm
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ NULL, NULL,
+ LLM_NORM, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+ if (hparams.f_clamp_kqv > 0.0f) {
+ Qcur = ggml_clamp(ctx0, Qcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+ cb(Qcur, "Qcur", il);
+ }
+
+ struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+ if (hparams.f_clamp_kqv > 0.0f) {
+ Kcur = ggml_clamp(ctx0, Kcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+ cb(Kcur, "Kcur", il);
+ }
+
+ struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+ if (hparams.f_clamp_kqv > 0.0f) {
+ Vcur = ggml_clamp(ctx0, Vcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+ cb(Vcur, "Vcur", il);
+ }
+
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, nullptr,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ n_tokens = n_outputs;
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ 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,
+ NULL, NULL,
+ LLM_NORM, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cb(cur, "ffn_out", il);
+
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ NULL, NULL,
+ LLM_NORM, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_openelm() {
+ 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);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+ const int64_t n_head = hparams.n_head(il);
+ const int64_t n_head_kv = hparams.n_head_kv(il);
+ const int64_t n_head_qkv = 2*n_head_kv + n_head;
+
+ cur = inpL;
+ struct ggml_tensor * residual = cur;
+
+ // norm
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+ cb(cur, "wqkv", il);
+
+ cur = ggml_reshape_3d(ctx0, cur, n_embd_head_k, n_head_qkv, n_tokens);
+
+ struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, cur->nb[1], cur->nb[2], 0));
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*n_head));
+ cb(Kcur, "Kcur", il);
+
+ struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*(n_head+n_head_kv)));
+ cb(Vcur, "Vcur", il);
+
+ Qcur = llm_build_norm(ctx0, Qcur, hparams,
+ model.layers[il].attn_q_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(Qcur, "Qcur", il);
+
+ Kcur = llm_build_norm(ctx0, Kcur, hparams,
+ model.layers[il].attn_k_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(Kcur, "Kcur", il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, NULL, n_rot, rope_type, n_ctx_orig,
+ freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, NULL, n_rot, rope_type, n_ctx_orig,
+ freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ Vcur = ggml_reshape_2d(ctx0, Vcur, n_embd_head * n_head_kv, n_tokens);
+ cb(Qcur, "Vcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, NULL,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ residual = ggml_get_rows(ctx0, residual, inp_out_ids);
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ }
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network
+ {
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ // norm
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_gptneox() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+ 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
+ {
+ cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+ cb(cur, "wqkv", il);
+
+ cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+ cb(cur, "bqkv", il);
+
+ struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+ struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+ struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ }
+
+ // ffn
+ if (hparams.use_par_res) {
+ // attention and ffn are computed in parallel
+ // x = x + attn(ln1(x)) + ffn(ln2(x))
+
+ struct ggml_tensor * attn_out = cur;
+
+ cur = llm_build_norm(ctx0, inpL, 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, lctx, cur,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+ NULL, NULL, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ NULL,
+ LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+ cb(cur, "ffn_out", il);
+
+ cur = ggml_add(ctx0, cur, inpL);
+ cb(cur, "ffn_out", il);
+
+ cur = ggml_add(ctx0, cur, attn_out);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ } else {
+ // attention and ffn are computed sequentially
+ // x = x + attn(ln1(x))
+ // x = x + ffn(ln2(x))
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+ cb(ffn_inp, "ffn_inp", il);
+
+ 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, lctx, cur,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+ NULL, NULL, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ NULL,
+ LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
+ cb(cur, "ffn_out", il);
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+ }
+
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.output_norm,
+ model.output_norm_b,
+ LLM_NORM, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_arctic() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ // mutable variable, needed during the last layer of the computation to skip unused tokens
+ int32_t n_tokens = this->n_tokens;
+
+ 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, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ 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, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ // compute Q and K and RoPE them
+ struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+
+ struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, NULL,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ n_tokens = n_outputs;
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ 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, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+
+ struct ggml_tensor * ffn_out = ggml_add(ctx0, cur, ffn_inp);
+ cb(ffn_out, "ffn_out", il);
+
+ // MoE
+ cur = llm_build_norm(ctx0, inpSA, hparams,
+ model.layers[il].ffn_norm_exps, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm_exps", il);
+
+ cur = llm_build_moe_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_gate_inp,
+ model.layers[il].ffn_up_exps,
+ model.layers[il].ffn_gate_exps,
+ model.layers[il].ffn_down_exps,
+ n_expert, n_expert_used,
+ LLM_FFN_SILU, true,
+ false, 0.0,
+ cb, il);
+ cb(cur, "ffn_moe_out", il);
+
+ cur = ggml_add(ctx0, cur, ffn_out);
+ cb(cur, "ffn_out", il);
+
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_deepseek2() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ // mutable variable, needed during the last layer of the computation to skip unused tokens
+ int32_t n_tokens = this->n_tokens;
+
+ bool is_lite = (hparams.n_layer == 27);
+
+ // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
+ // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation.
+ const float mscale = attn_factor * (1.0f + hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));
+ const float kq_scale = 1.0f*mscale*mscale/sqrtf(float(hparams.n_embd_head_k));
+ const float attn_factor_scaled = 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale));
+
+ const uint32_t n_embd_head_qk_rope = hparams.n_rot;
+ const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
+ const uint32_t kv_lora_rank = hparams.n_lora_kv;
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ // {n_embd, n_tokens}
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ 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, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self_attention
+ {
+ struct ggml_tensor * q = NULL;
+ if (!is_lite) {
+ // {n_embd, q_lora_rank} * {n_embd, n_tokens} -> {q_lora_rank, n_tokens}
+ q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
+ cb(q, "q", il);
+
+ q = llm_build_norm(ctx0, q, hparams,
+ model.layers[il].attn_q_a_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(q, "q", il);
+
+ // {q_lora_rank, n_head * hparams.n_embd_head_k} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k, n_tokens}
+ q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
+ cb(q, "q", il);
+ } else {
+ q = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+ cb(q, "q", il);
+ }
+
+ // split into {n_head * n_embd_head_qk_nope, n_tokens}
+ struct ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
+ ggml_row_size(q->type, hparams.n_embd_head_k),
+ ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
+ 0);
+ cb(q_nope, "q_nope", il);
+
+ // and {n_head * n_embd_head_qk_rope, n_tokens}
+ struct ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
+ ggml_row_size(q->type, hparams.n_embd_head_k),
+ ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
+ ggml_row_size(q->type, n_embd_head_qk_nope));
+ cb(q_pe, "q_pe", il);
+
+ // {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens}
+ struct ggml_tensor * kv_pe_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
+ cb(kv_pe_compresseed, "kv_pe_compresseed", il);
+
+ // split into {kv_lora_rank, n_tokens}
+ struct ggml_tensor * kv_compressed = ggml_view_2d(ctx0, kv_pe_compresseed, kv_lora_rank, n_tokens,
+ kv_pe_compresseed->nb[1],
+ 0);
+ cb(kv_compressed, "kv_compressed", il);
+
+ // and {n_embd_head_qk_rope, n_tokens}
+ struct ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_pe_compresseed, n_embd_head_qk_rope, 1, n_tokens,
+ kv_pe_compresseed->nb[1],
+ kv_pe_compresseed->nb[1],
+ ggml_row_size(kv_pe_compresseed->type, kv_lora_rank));
+ cb(k_pe, "k_pe", il);
+
+ kv_compressed = ggml_cont(ctx0, kv_compressed); // TODO: the CUDA backend does not support non-contiguous norm
+ kv_compressed = llm_build_norm(ctx0, kv_compressed, hparams,
+ model.layers[il].attn_kv_a_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(kv_compressed, "kv_compressed", il);
+
+ // {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)} * {kv_lora_rank, n_tokens} -> {n_head * (n_embd_head_qk_nope + n_embd_head_v), n_tokens}
+ struct ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_compressed);
+ cb(kv, "kv", il);
+
+ // split into {n_head * n_embd_head_qk_nope, n_tokens}
+ struct ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
+ ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v),
+ ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
+ 0);
+ cb(k_nope, "k_nope", il);
+
+ // and {n_head * n_embd_head_v, n_tokens}
+ struct ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens,
+ ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)),
+ ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head),
+ ggml_row_size(kv->type, (n_embd_head_qk_nope)));
+ cb(v_states, "v_states", il);
+
+ v_states = ggml_cont(ctx0, v_states);
+ cb(v_states, "v_states", il);
+
+ v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v * n_head, n_tokens,
+ ggml_row_size(kv->type, hparams.n_embd_head_v * n_head),
+ 0);
+ cb(v_states, "v_states", il);
+
+ q_pe = ggml_cont(ctx0, q_pe); // TODO: the CUDA backend does not support non-contiguous RoPE
+ q_pe = ggml_rope_ext(
+ ctx0, q_pe, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor_scaled, beta_fast, beta_slow
+ );
+ cb(q_pe, "q_pe", il);
+
+ // shared RoPE key
+ k_pe = ggml_cont(ctx0, k_pe); // TODO: the CUDA backend does not support non-contiguous RoPE
+ k_pe = ggml_rope_ext(
+ ctx0, k_pe, inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor_scaled, beta_fast, beta_slow
+ );
+ cb(k_pe, "k_pe", il);
+
+ struct ggml_tensor * q_states = ggml_concat(ctx0, q_nope, q_pe, 0);
+ cb(q_states, "q_states", il);
+
+ struct ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);
+ cb(k_states, "k_states", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, NULL,
+ k_states, v_states, q_states, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ n_tokens = n_outputs;
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ if ((uint32_t) il < hparams.n_layer_dense_lead) {
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+ } else {
+ // MoE branch
+ ggml_tensor * moe_out =
+ llm_build_moe_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_gate_inp,
+ model.layers[il].ffn_up_exps,
+ model.layers[il].ffn_gate_exps,
+ model.layers[il].ffn_down_exps,
+ n_expert, n_expert_used,
+ LLM_FFN_SILU, false,
+ true, hparams.expert_weights_scale,
+ cb, il);
+ cb(moe_out, "ffn_moe_out", il);
+
+ // FFN shared expert
+ {
+ ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up_shexp, NULL, NULL,
+ model.layers[il].ffn_gate_shexp, NULL, NULL,
+ model.layers[il].ffn_down_shexp, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(ffn_shexp, "ffn_shexp", il);
+
+ cur = ggml_add(ctx0, moe_out, ffn_shexp);
+ cb(cur, "ffn_out", il);
+ }
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cur = lctx.cvec.apply_to(ctx0, cur, il);
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, 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_bitnet() {
+ 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);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * inpSA = inpL;
+
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, 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);
+ float q_scale; std::memcpy(&q_scale, model.layers[il].wq->op_params, sizeof(float));
+ // Note: we could save this scale operation by applying the Q scale on the K * Q product further down
+ // (which also uses a scale). This works on the CPU and Metal backends, but produces NaNs on CUDA.
+ Qcur = ggml_scale(ctx0, Qcur, q_scale);
+ cb(Qcur, "Qcur", il);
+ if (model.layers[il].bq) {
+ Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+ cb(Qcur, "Qcur", il);
+ }
+
+ // B1.K
+ struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+ float k_scale; std::memcpy(&k_scale, model.layers[il].wk->op_params, sizeof(float));
+ Kcur = ggml_scale(ctx0, Kcur, k_scale);
+ cb(Kcur, "Kcur", il);
+ if (model.layers[il].bk) {
+ Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+ cb(Kcur, "Kcur", il);
+ }
+
+ // B1.V
+ struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+ float v_scale; std::memcpy(&v_scale, model.layers[il].wv->op_params, sizeof(float));
+ cb(Vcur, "Vcur", il);
+ if (model.layers[il].bv) {
+ Vcur = ggml_scale(ctx0, Vcur, v_scale);
+ Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+ cb(Vcur, "Vcur", il);
+ v_scale = 1;
+ }
+
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur", il);
+
+ llm_build_kv_store(ctx0, hparams, cparams, kv_self, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
+
+ const int64_t n_ctx = cparams.n_ctx;
+ const int64_t n_head = hparams.n_head();
+ const int64_t n_head_kv = hparams.n_head_kv();
+ const int64_t n_embd_head_k = hparams.n_embd_head_k;
+ const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
+ const int64_t n_embd_head_v = hparams.n_embd_head_v;
+ const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
+
+ float kq_scale = 1.0f/sqrtf(float(n_embd_head));
+ // We would use this if we did not apply the Q scale above. Sadly, this fails on CUDA.
+ //float kq_scale = q_scale/sqrtf(float(n_embd_head));
+ struct ggml_tensor * cur_attn;
+ struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+ cb(q, "q", il);
+
+ struct ggml_tensor * k =
+ ggml_view_3d(ctx0, kv_self.k_l[il],
+ n_embd_head_k, n_kv, n_head_kv,
+ ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
+ ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
+ 0);
+ cb(k, "k", il);
+
+ if (cparams.flash_attn) {
+
+ // split cached v into n_head heads (not transposed)
+ struct ggml_tensor * v =
+ ggml_view_3d(ctx0, kv_self.v_l[il],
+ n_embd_head_v, n_kv, n_head_kv,
+ ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa),
+ ggml_row_size(kv_self.v_l[il]->type, n_embd_head_v),
+ 0);
+ cb(v, "v", il);
+
+ cur_attn = ggml_flash_attn_ext(ctx0, q, k, v, KQ_mask, kq_scale, hparams.f_max_alibi_bias);
+
+ cur_attn = ggml_reshape_2d(ctx0, cur, n_embd_head_v*n_head, n_tokens);
+ } else {
+ struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+ cb(kq, "kq", il);
+
+ kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, kq_scale, hparams.f_max_alibi_bias);
+ cb(kq, "kq_soft_max_ext", il);
+
+ GGML_ASSERT(kv_self.size == n_ctx);
+
+ // split cached v into n_head heads
+ struct ggml_tensor * v =
+ ggml_view_3d(ctx0, kv_self.v_l[il],
+ n_kv, n_embd_head_v, n_head_kv,
+ ggml_element_size(kv_self.v_l[il])*n_ctx,
+ ggml_element_size(kv_self.v_l[il])*n_ctx*n_embd_head_v,
+ 0);
+ cb(v, "v", il);
+
+ struct ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
+ cb(kqv, "kqv", il);
+
+ struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+ cb(kqv_merged, "kqv_merged", il);
+
+ cur_attn = ggml_cont_2d(ctx0, kqv_merged, n_embd_head_v*n_head, n_tokens);
+ cb(cur_attn, "kqv_merged_cont", il);
+ }
+
+ cur_attn = llm_build_norm(ctx0, cur_attn, hparams,
+ model.layers[il].attn_sub_norm, NULL,
+ LLM_NORM_RMS, cb, il, 1/(v_scale*v_scale));
+ cb(cur_attn, "attn_sub_norm", il);
+
+ ggml_build_forward_expand(gf, cur_attn);
+
+ cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur_attn);
+ float wo_scale; std::memcpy(&wo_scale, model.layers[il].wo->op_params, sizeof(float));
+ cur = ggml_scale(ctx0, cur, wo_scale);
+
+ cb(cur, "kqv_out", il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward forward
+ if (model.layers[il].ffn_gate_inp == nullptr) {
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ struct ggml_tensor *tmp = ggml_mul_mat(ctx0, model.layers[il].ffn_up, cur);
+ float ffn_up_scale; std::memcpy(&ffn_up_scale, model.layers[il].ffn_up->op_params, sizeof(float));
+
+ cb(tmp, "ffn_up", il);
+
+ cur = ggml_mul_mat(ctx0, model.layers[il].ffn_gate, cur);
+ float ffn_gate_scale; std::memcpy(&ffn_gate_scale, model.layers[il].ffn_gate->op_params, sizeof(float));
+ cur = ggml_scale(ctx0, cur, ffn_gate_scale);
+
+ cb(cur, "ffn_gate", il);
+
+
+ // combine this with the above scale into ggml_scaled_silu
+ cur = ggml_silu(ctx0, cur);
+ cb(cur, "ffn_silu", il);
+
+ cur = ggml_mul(ctx0, cur, tmp);
+ cb(cur, "ffn_gate_par", il);
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.layers[il].ffn_sub_norm, NULL,
+ LLM_NORM_RMS, cb, il, 1/(ffn_up_scale*ffn_up_scale));
+ cb(cur, "ffn_sub_norm", il);
+
+ cur = ggml_mul_mat(ctx0, model.layers[il].ffn_down, cur);
+ float ffn_down_scale; std::memcpy(&ffn_down_scale, model.layers[il].ffn_down->op_params, sizeof(float));
+ cur = ggml_scale(ctx0, cur, ffn_down_scale);
+ cb(cur, "ffn_down", 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, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+ return gf;
+ }
+
+ struct ggml_cgraph * build_t5() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ // mutable variable, needed during the last layer of the computation to skip unused tokens
+ int32_t n_tokens = this->n_tokens;
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ if (lctx.is_encoding) {
+ struct ggml_tensor * pos_bucket_enc = llm_build_pos_bucket(false);
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask_enc = build_inp_KQ_mask(false);
+
+ 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_enc, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq_enc, cur);
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk_enc, cur);
+ cb(Kcur, "Kcur", il);
+
+ struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv_enc, cur);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+
+ struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+ struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
+
+ struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+ cb(kq, "kq", il);
+
+ struct ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b_enc ? model.layers[il].attn_rel_b_enc : model.layers[0].attn_rel_b_enc;
+ struct ggml_tensor * pos_bias = llm_build_pos_bias(pos_bucket_enc, attn_rel_b);
+ struct ggml_tensor * kq_b = ggml_add(ctx0, kq, pos_bias);
+ cb(kq_b, "kq_b", il);
+
+ kq = ggml_soft_max_ext(ctx0, kq_b, KQ_mask_enc, 1.0f, hparams.f_max_alibi_bias);
+ cb(kq, "kq_soft_max_ext", il);
+
+ struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens)));
+ cb(v, "v", il);
+
+ struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_tokens, n_embd_head, n_head_kv), kq);
+ cb(kqv, "kqv", il);
+
+ struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+ cb(kqv_merged, "kqv_merged", il);
+
+ cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
+ cb(cur, "kqv_merged_cont", il);
+
+ ggml_build_forward_expand(gf, cur);
+
+ cur = ggml_mul_mat(ctx0, model.layers[il].wo_enc, cur);
+ cb(cur, "kqv_out", il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ n_tokens = n_outputs;
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ 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_enc, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ // T5 uses relu, flan-T5 uses gelu-gated
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up_enc, NULL, NULL,
+ model.layers[il].ffn_gate_enc, NULL, NULL,
+ model.layers[il].ffn_down_enc, NULL, NULL,
+ NULL,
+ model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
+ model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ,
+ cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cb(cur, "ffn_out", il);
+
+ ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
+ if (layer_dir != nullptr) {
+ cur = ggml_add(ctx0, cur, layer_dir);
+ }
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+ cb(cur, "result_embd", -1);
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm_enc, NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+ } else {
+ GGML_ASSERT(n_outputs_enc > 0 && "call llama_encode() first");
+
+ struct ggml_tensor * embd_enc = llm_build_inp_embd_enc();
+ struct ggml_tensor * pos_bucket_dec = llm_build_pos_bucket(true);
+
+ struct ggml_tensor * KQ_mask_dec = build_inp_KQ_mask();
+ struct ggml_tensor * KQ_mask_cross = llm_build_inp_KQ_mask_cross();
+
+ 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, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+
+ struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+
+ llm_build_kv_store(ctx0, hparams, cparams, kv_self, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
+
+ struct ggml_tensor * k =
+ ggml_view_3d(ctx0, kv_self.k_l[il],
+ n_embd_head_k, n_kv, n_head_kv,
+ ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
+ ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
+ 0);
+ cb(k, "k", il);
+
+ struct ggml_tensor * v =
+ ggml_view_3d(ctx0, kv_self.v_l[il],
+ n_kv, n_embd_head_v, n_head_kv,
+ ggml_element_size(kv_self.v_l[il])*n_ctx,
+ ggml_element_size(kv_self.v_l[il])*n_ctx*n_embd_head_v,
+ 0);
+ cb(v, "v", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+
+ struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+
+ struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+ cb(kq, "kq", il);
+
+ struct ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b ? model.layers[il].attn_rel_b : model.layers[0].attn_rel_b;
+ struct ggml_tensor * pos_bias = llm_build_pos_bias(pos_bucket_dec, attn_rel_b);
+ struct ggml_tensor * kq_b = ggml_add(ctx0, kq, pos_bias);
+ cb(kq_b, "kq_b", il);
+
+ kq = ggml_soft_max_ext(ctx0, kq_b, KQ_mask_dec, 1.0f, hparams.f_max_alibi_bias);
+ cb(kq, "kq_soft_max_ext", il);
+
+ struct ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
+ cb(kqv, "kqv", il);
+
+ struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+ cb(kqv_merged, "kqv_merged", il);
+
+ cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
+ cb(cur, "kqv_merged_cont", il);
+
+ ggml_build_forward_expand(gf, cur);
+
+ cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
+ cb(cur, "kqv_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, inpSA);
+ cb(cur, "cross_inp", il);
+
+ struct ggml_tensor * inpCA = cur;
+
+ // norm
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.layers[il].attn_norm_cross, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm_cross", il);
+
+ // cross-attention
+ {
+ struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq_cross, cur);
+ cb(Qcur, "Qcur", il);
+
+ struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk_cross, embd_enc);
+ cb(Kcur, "Kcur", il);
+
+ struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv_cross, embd_enc);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_outputs_enc);
+
+ struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+ struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
+
+ struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+ cb(kq, "kq", il);
+
+ kq = ggml_soft_max_ext(ctx0, kq, KQ_mask_cross, 1.0f, hparams.f_max_alibi_bias);
+ cb(kq, "kq_soft_max_ext", il);
+
+ struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_outputs_enc)));
+ cb(v, "v", il);
+
+ struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_outputs_enc, n_embd_head, n_head_kv), kq);
+ cb(kqv, "kqv", il);
+
+ struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+ cb(kqv_merged, "kqv_merged", il);
+
+ cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
+ cb(cur, "kqv_merged_cont", il);
+
+ ggml_build_forward_expand(gf, cur);
+
+ cur = ggml_mul_mat(ctx0, model.layers[il].wo_cross, cur);
+ cb(cur, "kqv_out", il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ n_tokens = n_outputs;
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ inpCA = ggml_get_rows(ctx0, inpCA, inp_out_ids);
+ }
+
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpCA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // feed-forward network
+ {
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ // T5 uses relu, flan-T5 uses gelu-gated
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
+ model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ,
+ cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cb(cur, "ffn_out", il);
+
+ ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
+ if (layer_dir != nullptr) {
+ cur = ggml_add(ctx0, cur, layer_dir);
+ }
+ cb(cur, "l_out", il);
+
+ // input for next layer
+ inpL = cur;
+ }
+
+ cur = inpL;
+ cb(cur, "result_embd", -1);
+
+ cur = llm_build_norm(ctx0, cur, hparams,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, 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_jais() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+ 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
+ {
+ cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+ cb(cur, "wqkv", il);
+
+ cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+ cb(cur, "bqkv", il);
+
+ struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*cur->nb[0]*(n_embd)));
+ struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*cur->nb[0]*(n_embd)));
+ struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*cur->nb[0]*(n_embd + n_embd_gqa)));
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, model.layers[il].bo,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/float(n_embd_head), cb, il);
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+ }
+
+ // add the input
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // FF
+ {
+ 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, lctx, cur,
+ model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+ model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+ model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+ cb(cur, "ffn_out", il);
+ }
+
+ inpL = ggml_add(ctx0, cur, ffn_inp);
+ cb(inpL, "l_out", il);
+ }
+
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.output_norm,
+ model.output_norm_b,
+ LLM_NORM, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+
+ struct ggml_cgraph * build_chatglm() {
+ struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+ const int64_t n_embd_head = hparams.n_embd_head_v;
+ const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+ struct ggml_tensor * cur;
+ struct ggml_tensor * inpL;
+
+ inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+ // inp_pos - contains the positions
+ struct ggml_tensor * inp_pos = build_inp_pos();
+
+ // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+ struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+ for (int il = 0; il < n_layer; ++il) {
+ struct ggml_tensor * inpSA = inpL;
+
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.layers[il].attn_norm,
+ NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "attn_norm", il);
+
+ // self-attention
+ {
+ struct ggml_tensor * Qcur = nullptr;
+ struct ggml_tensor * Kcur = nullptr;
+ struct ggml_tensor * Vcur = nullptr;
+
+ cur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wqkv, cur);
+ cb(cur, "wqkv", il);
+
+ cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+ cb(cur, "bqkv", il);
+
+ Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd)));
+ Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd)));
+ Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa)));
+
+ cb(Qcur, "Qcur", il);
+ cb(Kcur, "Kcur", il);
+ cb(Vcur, "Vcur", il);
+ //printf("freq_base: %f freq_scale: %f ext_factor: %f attn_factor: %f\n", freq_base, freq_scale, ext_factor, attn_factor);
+ Qcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Qcur, "Qcur_rope", il);
+
+ Kcur = ggml_rope_ext(
+ ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ cb(Kcur, "Kcur_rope", il);
+
+ cur = llm_build_kv(ctx0, lctx, kv_self, gf,
+ model.layers[il].wo, NULL,
+ Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+
+ }
+
+ if (il == n_layer - 1) {
+ // skip computing output for unused tokens
+ struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+ }
+
+ // Add the input
+ struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // FF
+ {
+ cur = llm_build_norm(ctx0, ffn_inp, hparams,
+ model.layers[il].ffn_norm,
+ NULL,
+ LLM_NORM_RMS, cb, il);
+ cb(cur, "ffn_norm", il);
+
+ cur = llm_build_ffn(ctx0, lctx, cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ NULL, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SWIGLU, LLM_FFN_SEQ, cb, il);
+ cb(cur, "ffn_out", il);
+
+ }
+
+ inpL = ggml_add(ctx0, cur, ffn_inp);
+ cb(inpL, "l_out", il);
+ }
+
+ cur = llm_build_norm(ctx0, inpL, hparams,
+ model.output_norm,
+ NULL,
+ LLM_NORM_RMS, cb, -1);
+ cb(cur, "result_norm", -1);
+
+ cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
+ cb(cur, "result_output", -1);
+
+ ggml_build_forward_expand(gf, cur);
+
+ return gf;
+ }
+};
+
+static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
+ llama_batch dummy;
+ dummy.n_tokens = 0;
+
+ llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
+
+ struct llm_build_context llm(lctx, dummy, cb, false);
+
+ llm.init();
+
+ struct ggml_cgraph * result = llm.build_defrag(ids);
+
+ llm.free();
+
+ return result;
+}
+
+static struct ggml_cgraph * llama_build_graph_k_shift(llama_context & lctx) {
+ llama_batch dummy;
+ dummy.n_tokens = 0;
+
+ llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
+
+ struct llm_build_context llm(lctx, dummy, cb, false);
+
+ llm.init();
+
+ struct ggml_cgraph * result = llm.build_k_shift();
+
+ llm.free();
+
+ return result;
+}
+
+static struct ggml_cgraph * llama_build_graph_s_copy(llama_context & lctx) {
+ llama_batch dummy;
+ dummy.n_tokens = 0;
+
+ llm_build_cb cb = [&](struct ggml_tensor * , const char * , int ) { };
+
+ struct llm_build_context llm(lctx, dummy, cb, false);
+
+ llm.init();
+
+ struct ggml_cgraph * result = llm.build_s_copy();
+
+ llm.free();
+
+ return result;
+}
+
+static struct ggml_cgraph * llama_build_graph(
+ llama_context & lctx,
+ const llama_batch & batch,
+ bool worst_case) {
+ const auto & model = lctx.model;
+
+ // this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.)
+ llm_build_cb cb = [&](struct ggml_tensor * cur, const char * name, int il) {
+ if (il >= 0) {
+ ggml_format_name(cur, "%s-%d", name, il);
+ } else {
+ ggml_set_name(cur, name);
+ }
+
+ if (!lctx.cparams.offload_kqv) {
+ if (strcmp(name, "kqv_merged_cont") == 0) {
+ // all nodes between the KV store and the attention output are run on the CPU
+ ggml_backend_sched_set_tensor_backend(lctx.sched, cur, lctx.backend_cpu);
+ }
+ }
+
+ // norm may be automatically assigned to the backend of the previous layer, increasing data transfer between backends
+ // FIXME: fix in ggml_backend_sched
+ const bool full_offload = lctx.model.n_gpu_layers > (int)lctx.model.hparams.n_layer;
+ if (batch.n_tokens < 32 || full_offload) {
+ if (il != -1 && strcmp(name, "norm") == 0) {
+ for (auto * backend : lctx.backends) {
+ if (ggml_backend_supports_buft(backend, lctx.model.buft_layer[il].buft) &&
+ (ggml_backend_supports_op(backend, cur) || ggml_backend_offload_op(backend, cur))) {
+ ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend);
+ break;
+ }
+ }
+ }
+ }
+ };
+
+ struct ggml_cgraph * result = NULL;
+
+ struct llm_build_context llm(lctx, batch, cb, worst_case);
+
+ llm.init();
+
+ switch (model.arch) {
+ case LLM_ARCH_LLAMA:
+ {
+ result = llm.build_llama();
+ } break;
+ case LLM_ARCH_BAICHUAN:
+ {
+ result = llm.build_baichuan();
+ } break;
+ case LLM_ARCH_FALCON:
+ {
+ result = llm.build_falcon();
+ } break;
+ case LLM_ARCH_GROK:
+ {
+ result = llm.build_grok();
+ } break;
+ case LLM_ARCH_STARCODER:
+ {
+ result = llm.build_starcoder();
+ } break;
+ case LLM_ARCH_REFACT:
+ {
+ result = llm.build_refact();
+ } break;
+ case LLM_ARCH_BERT:
+ case LLM_ARCH_JINA_BERT_V2:
+ case LLM_ARCH_NOMIC_BERT:
+ {
+ result = llm.build_bert();
+ } break;
+ case LLM_ARCH_BLOOM:
+ {
+ result = llm.build_bloom();
+ } break;
+ case LLM_ARCH_MPT:
+ {
+ result = llm.build_mpt();
+ } break;
+ case LLM_ARCH_STABLELM:
+ {
+ result = llm.build_stablelm();
+ } break;
+ case LLM_ARCH_QWEN:
+ {
+ result = llm.build_qwen();
+ } break;
+ case LLM_ARCH_QWEN2:
+ {
+ result = llm.build_qwen2();
+ } break;
+ case LLM_ARCH_QWEN2MOE:
+ {
+ result = llm.build_qwen2moe();
+ } break;
+ case LLM_ARCH_PHI2:
+ {
+ result = llm.build_phi2();
+ } break;
+ case LLM_ARCH_PHI3:
+ {
+ result = llm.build_phi3();
+ } break;
+ case LLM_ARCH_PLAMO:
+ {
+ result = llm.build_plamo();
+ } break;
+ case LLM_ARCH_GPT2:
+ {
+ result = llm.build_gpt2();
+ } break;
+ case LLM_ARCH_CODESHELL:
+ {
+ result = llm.build_codeshell();
+ } break;
+ case LLM_ARCH_ORION:
+ {
+ result = llm.build_orion();
+ } break;
+ case LLM_ARCH_INTERNLM2:
+ {
+ result = llm.build_internlm2();
+ } break;
+ case LLM_ARCH_MINICPM:
+ {
+ result = llm.build_minicpm();
+ } break;
+ case LLM_ARCH_GEMMA:
+ {
+ result = llm.build_gemma();
+ } break;
+ case LLM_ARCH_GEMMA2:
+ {
+ result = llm.build_gemma2();
+ } break;
+ case LLM_ARCH_STARCODER2:
+ {
+ result = llm.build_starcoder2();
+ } break;
+ case LLM_ARCH_MAMBA:
+ {
+ result = llm.build_mamba();
+ } break;
+ case LLM_ARCH_XVERSE:
+ {
+ result = llm.build_xverse();
+ } break;
+ case LLM_ARCH_COMMAND_R:
+ {
+ result = llm.build_command_r();
+ } break;
+ case LLM_ARCH_DBRX:
+ {
+ result = llm.build_dbrx();
+ } break;
+ case LLM_ARCH_OLMO:
+ {
+ result = llm.build_olmo();
+ } break;
+ case LLM_ARCH_OPENELM:
+ {
+ result = llm.build_openelm();
+ } break;
+ case LLM_ARCH_GPTNEOX:
+ {
+ result = llm.build_gptneox();
+ } break;
+ case LLM_ARCH_ARCTIC:
+ {
+ result = llm.build_arctic();
+ } break;
+ case LLM_ARCH_DEEPSEEK2:
+ {
+ result = llm.build_deepseek2();
+ } break;
+ case LLM_ARCH_CHATGLM:
+ {
+ result = llm.build_chatglm();
+ } break;
+ case LLM_ARCH_BITNET:
+ {
+ result = llm.build_bitnet();
+ } break;
+ case LLM_ARCH_T5:
+ {
+ result = llm.build_t5();
+ } break;
+ case LLM_ARCH_JAIS:
+ {
+ result = llm.build_jais();
+ } break;
+ default:
+ GGML_ASSERT(false);
+ }
+
+ // add on pooling layer
+ if (lctx.cparams.embeddings) {
+ result = llm.append_pooling(result);
+ }
+
+ llm.free();
+
+ return result;
+}
+
+static void llama_set_k_shift(llama_context & lctx) {
+ const int64_t kv_size = lctx.kv_self.size;
+
+ assert(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer));
+
+ int32_t * data = (int32_t *) lctx.inp_K_shift->data;
+
+ for (int i = 0; i < kv_size; ++i) {
+ data[i] = lctx.kv_self.cells[i].delta;
+ }
+}
+
+static void llama_set_s_copy(llama_context & lctx) {
+ const int64_t kv_size = lctx.kv_self.size;
+
+ assert(ggml_backend_buffer_is_host(lctx.inp_s_copy->buffer));
+
+ int32_t * data = (int32_t *) lctx.inp_s_copy->data;
+
+ for (int i = 0; i < kv_size; ++i) {
+ data[i] = lctx.kv_self.cells[i].src;
+ }
+}
+
+static int32_t llama_relative_position_bucket(llama_pos x, llama_pos y, uint64_t n_buckets, bool bidirectional) {
+ // TODO move to hparams if a T5 variant appears that uses a different value
+ const int64_t max_distance = 128;
+
+ if (bidirectional) {
+ n_buckets >>= 1;
+ }
+
+ const int64_t max_exact = n_buckets >> 1;
+
+ int32_t relative_position = x - y;
+ int32_t relative_bucket = 0;
+ if (bidirectional) {
+ relative_bucket += (relative_position > 0) * n_buckets;
+ relative_position = abs(relative_position);
+ } else {
+ relative_position = -std::min<int32_t>(relative_position, 0);
+ }
+ int32_t relative_position_if_large = floorf(max_exact + logf(1.0 * relative_position / max_exact) * (n_buckets - max_exact) / log(1.0 * max_distance / max_exact));
+ relative_position_if_large = std::min<int32_t>(relative_position_if_large, n_buckets - 1);
+ relative_bucket += (relative_position < max_exact ? relative_position : relative_position_if_large);
+ return relative_bucket;
+}
+
+static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) {
+ //
+ // set input data
+ //
+
+ const auto & hparams = lctx.model.hparams;
+ const auto & cparams = lctx.cparams;
+ const auto & kv_self = lctx.kv_self;
+
+ if (batch.token) {
+ const int64_t n_tokens = batch.n_tokens;
+
+ ggml_backend_tensor_set(lctx.inp_tokens, batch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens));
+ }
+
+ if (batch.embd) {
+ const int64_t n_embd = hparams.n_embd;
+ const int64_t n_tokens = batch.n_tokens;
+
+ ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd));
+ }
+
+ if (batch.pos && lctx.inp_pos) {
+ const int64_t n_tokens = batch.n_tokens;
+
+ ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos));
+ }
+
+ if (hparams.causal_attn || cparams.pooling_type == LLAMA_POOLING_TYPE_NONE) {
+ GGML_ASSERT(lctx.inp_out_ids && "every model that can must skip unused outputs");
+ const int64_t n_tokens = batch.n_tokens;
+
+ GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_out_ids->buffer));
+ int32_t * data = (int32_t *) lctx.inp_out_ids->data;
+
+ if (lctx.n_outputs == n_tokens) {
+ for (int i = 0; i < n_tokens; ++i) {
+ data[i] = i;
+ }
+ } else if (batch.logits) {
+ int32_t n_outputs = 0;
+ for (int i = 0; i < n_tokens; ++i) {
+ if (batch.logits[i]) {
+ data[n_outputs++] = i;
+ }
+ }
+ // the graph needs to have been passed the correct number of outputs
+ GGML_ASSERT(lctx.n_outputs == n_outputs);
+ } else if (lctx.n_outputs == 1) {
+ // only keep last output
+ data[0] = n_tokens - 1;
+ } else {
+ GGML_ASSERT(lctx.n_outputs == 0);
+ }
+ }
+
+ GGML_ASSERT(
+ // (!a || b) is a logical implication (a -> b)
+ // !hparams.causal_attn -> !cparams.causal_attn
+ (hparams.causal_attn || !cparams.causal_attn) &&
+ "causal attention is not supported by this model"
+ );
+
+ if (lctx.inp_KQ_mask || lctx.inp_KQ_mask_swa) {
+ // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache.
+ if (cparams.causal_attn && !lctx.is_encoding) {
+ const int64_t n_kv = kv_self.n;
+ const int64_t n_tokens = batch.n_tokens;
+
+
+ float * data = nullptr;
+ float * data_swa = nullptr;
+
+ if (lctx.inp_KQ_mask) {
+ GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
+ data = (float *) lctx.inp_KQ_mask->data;
+ }
+
+ if (lctx.inp_KQ_mask_swa) {
+ GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_swa->buffer));
+ data_swa = (float *) lctx.inp_KQ_mask_swa->data;
+ }
+
+ // For causal attention, use only the previous KV cells
+ // of the correct sequence for each token of the batch.
+ // It's assumed that if a token in the batch has multiple sequences, they are equivalent.
+ for (int h = 0; h < 1; ++h) {
+ for (int j = 0; j < n_tokens; ++j) {
+ const llama_pos pos = batch.pos[j];
+ const llama_seq_id seq_id = batch.seq_id[j][0];
+
+ for (int i = 0; i < n_kv; ++i) {
+ float f;
+ if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) {
+ f = -INFINITY;
+ } else {
+ if (hparams.use_alibi) {
+ f = -std::abs(lctx.kv_self.cells[i].pos - pos);
+ } else {
+ f = 0.0f;
+ }
+ }
+
+ if (data) {
+ data[h*(n_kv*n_tokens) + j*n_kv + i] = f;
+ }
+
+ // may need to cut off old tokens for sliding window
+ if (data_swa) {
+ if (pos - lctx.kv_self.cells[i].pos >= (int32_t)hparams.n_swa) {
+ f = -INFINITY;
+ }
+ data_swa[h*(n_kv*n_tokens) + j*n_kv + i] = f;
+ }
+ }
+ }
+
+ if (data) {
+ for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
+ for (int j = 0; j < n_kv; ++j) {
+ data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
+ }
+ }
+ }
+
+ if (data_swa) {
+ for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
+ for (int j = 0; j < n_kv; ++j) {
+ data_swa[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY;
+ }
+ }
+ }
+ }
+ } else {
+ // when using kv cache, the mask needs to match the kv cache size
+ const int64_t n_tokens = batch.n_tokens;
+ const int64_t n_stride = hparams.causal_attn && !lctx.is_encoding ? kv_self.n : n_tokens;
+
+ GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
+
+ float * data = (float *) lctx.inp_KQ_mask->data;
+
+ for (int h = 0; h < 1; ++h) {
+ for (int j = 0; j < n_tokens; ++j) {
+ const llama_seq_id seq_id = batch.seq_id[j][0];
+
+ for (int i = 0; i < n_tokens; ++i) {
+ float f = -INFINITY;
+ for (int s = 0; s < batch.n_seq_id[i]; ++s) {
+ if (batch.seq_id[i][s] == seq_id) {
+ if (hparams.use_alibi) {
+ f = -std::abs(batch.pos[i] - batch.pos[j]);
+ } else {
+ f = 0.0f;
+ }
+ break;
+ }
+ }
+
+ data[h*(n_tokens*n_tokens) + j*n_stride + i] = f;
+ }
+
+ for (int i = n_tokens; i < n_stride; ++i) {
+ data[h*(n_tokens*n_tokens) + j*n_stride + i] = -INFINITY;
+ }
+ }
+ }
+ }
+ }
+
+ if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) {
+ const int64_t n_tokens = batch.n_tokens;
+
+ GGML_ASSERT(lctx.inp_mean);
+ GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_mean->buffer));
+
+ float * data = (float *) lctx.inp_mean->data;
+ memset(lctx.inp_mean->data, 0, n_tokens * n_tokens * ggml_element_size(lctx.inp_mean));
+
+ std::vector<uint64_t> sum(n_tokens, 0);
+ for (int i = 0; i < n_tokens; ++i) {
+ const llama_seq_id seq_id = batch.seq_id[i][0];
+
+ GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == MEAN");
+
+ sum[seq_id] += 1;
+ }
+
+ std::vector<float> div(n_tokens, 0.0f);
+ for (int i = 0; i < n_tokens; ++i) {
+ const uint64_t s = sum[i];
+ if (s > 0) {
+ div[i] = 1.0f/float(s);
+ }
+ }
+
+ for (int i = 0; i < n_tokens; ++i) {
+ const llama_seq_id seq_id = batch.seq_id[i][0];
+ data[seq_id*n_tokens + i] = div[seq_id];
+ }
+ }
+
+ if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
+ const int64_t n_tokens = batch.n_tokens;
+
+ GGML_ASSERT(lctx.inp_cls);
+ GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
+
+ uint32_t * data = (uint32_t *) lctx.inp_cls->data;
+ memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));
+
+ for (int i = 0; i < n_tokens; ++i) {
+ const llama_seq_id seq_id = batch.seq_id[i][0];
+ const llama_pos pos = batch.pos[i];
+
+ GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == CLS");
+
+ if (pos == 0) {
+ data[seq_id] = i;
+ }
+ }
+ }
+
+ if (cparams.embeddings && cparams.pooling_type == LLAMA_POOLING_TYPE_LAST) {
+ const int64_t n_tokens = batch.n_tokens;
+
+ GGML_ASSERT(lctx.inp_cls);
+ GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_cls->buffer));
+
+ uint32_t * data = (uint32_t *) lctx.inp_cls->data;
+ memset(lctx.inp_cls->data, 0, n_tokens * ggml_element_size(lctx.inp_cls));
+
+ std::vector<int> last_pos(n_tokens, -1);
+ std::vector<int> last_row(n_tokens, -1);
+
+ for (int i = 0; i < n_tokens; ++i) {
+ const llama_seq_id seq_id = batch.seq_id[i][0];
+ const llama_pos pos = batch.pos[i];
+
+ GGML_ASSERT(seq_id < n_tokens && "seq_id cannot be larger than n_tokens with pooling_type == LAST");
+
+ if (pos >= last_pos[seq_id]) {
+ last_pos[seq_id] = pos;
+ last_row[seq_id] = i;
+ }
+ }
+
+ for (int i = 0; i < n_tokens; ++i) {
+ if (last_row[i] >= 0) {
+ data[i] = last_row[i];
+ }
+ }
+ }
+
+ if (kv_self.recurrent) {
+ const int64_t n_kv = kv_self.n;
+
+ if (lctx.inp_s_mask) {
+ GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_mask->buffer));
+ float * data = (float *) lctx.inp_s_mask->data;
+
+ // states which are not affected by the current batch are left untouched
+ for (int i = 0; i < n_kv; ++i) {
+ llama_seq_id seq_id = i + lctx.kv_self.head;
+ llama_kv_cell & kv_cell = lctx.kv_self.cells[seq_id];
+ bool has_self_seq = kv_cell.has_seq_id(seq_id);
+
+ data[i] = (float) has_self_seq;
+
+ // ensure current sequences will be kept
+ if (!has_self_seq && kv_cell.pos >= 0) {
+ kv_cell.seq_id.insert(seq_id);
+ }
+ }
+ }
+ // For Mamba (and other recurrent architectures),
+ // update the correct state(s)/sequence(s) for each token of the batch.
+ // Like with the KQ_mask, if a token in the batch has multiple sequences,
+ // they are assumed to be equivalent (not here, but in ggml_ssm_scan and ggml_ssm_conv).
+ if (lctx.inp_s_seq) {
+ const int64_t n_tokens = batch.n_tokens;
+
+ GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_s_seq->buffer));
+ int32_t * data = (int32_t *) lctx.inp_s_seq->data;
+
+ for (int j = 0; j < n_tokens; ++j) {
+ const int32_t n_seq = batch.n_seq_id[j];
+ GGML_ASSERT(0 < n_seq); // a token should be part of at least 1 sequence
+
+ for (int i = 0; i < n_kv; ++i) {
+ if (i < n_seq) {
+ // for this type of model, the head is the minimum seq_id of the batch
+ data[j*n_kv + i] = batch.seq_id[j][i] - kv_self.head;
+ } else {
+ data[j*n_kv + i] = -1;
+ }
+ }
+ }
+ }
+ }
+
+ if (lctx.inp_pos_bucket) {
+ const int64_t n_tokens = batch.n_tokens;
+
+ GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_pos_bucket->buffer));
+
+ int32_t * data = (int32_t *) lctx.inp_pos_bucket->data;
+
+ if (!lctx.is_encoding) {
+ const int64_t n_kv = kv_self.n;
+ for (int h = 0; h < 1; ++h) {
+ for (int j = 0; j < n_tokens; ++j) {
+ for (int i = 0; i < n_kv; ++i) {
+ data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(lctx.kv_self.cells[i].pos, batch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding);
+ }
+ }
+ }
+ } else {
+ for (int h = 0; h < 1; ++h) {
+ for (int j = 0; j < n_tokens; ++j) {
+ for (int i = 0; i < n_tokens; ++i) {
+ data[h*(n_tokens*n_tokens) + j*n_tokens + i] = llama_relative_position_bucket(batch.pos[i], batch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding);
+ }
+ }
+ }
+ }
+ }
+
+ if (!lctx.is_encoding && lctx.inp_embd_enc) {
+ assert(lctx.inp_embd_enc->type == GGML_TYPE_F32);
+ assert((size_t) ggml_nelements(lctx.inp_embd_enc) == lctx.embd_enc.size());
+
+ ggml_backend_tensor_set(lctx.inp_embd_enc, lctx.embd_enc.data(), 0, ggml_nbytes(lctx.inp_embd_enc));
+ }
+
+ if (!lctx.is_encoding && lctx.inp_KQ_mask_cross) {
+ const int64_t n_output_enc = lctx.embd_enc.size() / hparams.n_embd;
+ const int64_t n_tokens = batch.n_tokens;
+
+ GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_cross->buffer));
+
+ float * data = (float *) lctx.inp_KQ_mask_cross->data;
+
+ for (int h = 0; h < 1; ++h) {
+ for (int j = 0; j < n_tokens; ++j) {
+ for (int i = 0; i < n_output_enc; ++i) {
+ float f = -INFINITY;
+ for (int s = 0; s < batch.n_seq_id[j]; ++s) {
+ const llama_seq_id seq_id = batch.seq_id[j][s];
+ if (lctx.seq_ids_enc[i].find(seq_id) != lctx.seq_ids_enc[i].end()) {
+ f = 0.0f;
+ }
+ }
+ data[h*(n_output_enc*n_tokens) + j*n_output_enc + i] = f;
+ }
+ }
+
+ for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
+ for (int j = 0; j < n_output_enc; ++j) {
+ data[h*(n_output_enc*n_tokens) + i*n_output_enc + j] = -INFINITY;
+ }
+ }
+ }
+ }
+}
+
+// Make sure enough space is available for outputs.
+// Returns max number of outputs for which space was reserved.
+static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
+ const auto & cparams = lctx.cparams;
+ const auto & hparams = lctx.model.hparams;
+
+ const size_t n_outputs_max = std::max(n_outputs, (size_t) cparams.n_seq_max);
+
+ const auto n_batch = cparams.n_batch;
+ const auto n_vocab = hparams.n_vocab;
+ const auto n_embd = hparams.n_embd;
+
+ // TODO: use a per-batch flag for logits presence instead
+ const bool has_logits = !cparams.embeddings;
+ const bool has_embd = lctx.is_encoding || (cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE));
+
+ const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0;
+ const size_t embd_size = has_embd ? n_embd*n_outputs_max : 0;
+
+ if (lctx.output_ids.empty()) {
+ // init, never resized afterwards
+ lctx.output_ids.resize(n_batch);
+ }
+
+ const size_t prev_size = lctx.buf_output ? ggml_backend_buffer_get_size(lctx.buf_output) : 0;
+ const size_t new_size = (logits_size + embd_size) * sizeof(float);
+
+ // alloc only when more than the current capacity is required
+ // TODO: also consider shrinking the buffer
+ if (!lctx.buf_output || prev_size < new_size) {
+ if (lctx.buf_output) {
+#ifndef NDEBUG
+ // This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark)
+ LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
+#endif
+ ggml_backend_buffer_free(lctx.buf_output);
+ lctx.buf_output = nullptr;
+ lctx.logits = nullptr;
+ lctx.embd = nullptr;
+ }
+
+ lctx.buf_output = ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), new_size);
+ if (lctx.buf_output == nullptr) {
+ LLAMA_LOG_ERROR("%s: failed to allocate output buffer of size %.2f MiB\n", __func__, new_size / (1024.0 * 1024.0));
+ return 0;
+ }
+ }
+
+ float * output_base = (float *) ggml_backend_buffer_get_base(lctx.buf_output);
+
+ lctx.logits = has_logits ? output_base : nullptr;
+ lctx.embd = has_embd ? output_base + logits_size : nullptr;
+
+ lctx.output_size = n_outputs_max;
+ lctx.logits_size = logits_size;
+ lctx.embd_size = embd_size;
+
+ // set all ids as invalid (negative)
+ std::fill(lctx.output_ids.begin(), lctx.output_ids.end(), -1);
+
+ ggml_backend_buffer_clear(lctx.buf_output, 0);
+
+ lctx.n_outputs = 0;
+
+ return n_outputs_max;
+}
+
+
+static void llama_graph_compute(
+ llama_context & lctx,
+ ggml_cgraph * gf,
+ int n_threads) {
+#ifdef GGML_USE_METAL
+ if (ggml_backend_is_metal(lctx.backend_metal)) {
+ ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads);
+ }
+#endif
+
+ if (lctx.backend_cpu != nullptr) {
+ ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads);
+ ggml_backend_cpu_set_abort_callback(lctx.backend_cpu, lctx.abort_callback, lctx.abort_callback_data);
+ }
+#ifdef GGML_USE_BLAS
+ if (lctx.backend_blas != nullptr) {
+ ggml_backend_blas_set_n_threads(lctx.backend_blas, n_threads);
+ }
+#endif
+
+ ggml_backend_sched_graph_compute_async(lctx.sched, gf);
+
+ // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched));
+}
+
+// decode a batch of tokens by evaluating the transformer
+//
+// - lctx: llama context
+// - batch: batch to evaluate
+//
+// return 0 on success
+// return positive int on warning
+// return negative int on error
+//
+static int llama_decode_internal(
+ llama_context & lctx,
+ llama_batch batch_all) { // TODO: rename back to batch
+
+ lctx.is_encoding = false;
+ const uint32_t n_tokens_all = batch_all.n_tokens;
+
+ if (n_tokens_all == 0) {
+ LLAMA_LOG_ERROR("%s: n_tokens == 0", __func__);
+ return -1;
+ }
+
+ const auto & model = lctx.model;
+ const auto & hparams = model.hparams;
+ const auto & cparams = lctx.cparams;
+
+ GGML_ASSERT((!batch_all.token && batch_all.embd) || (batch_all.token && !batch_all.embd)); // NOLINT
+
+ GGML_ASSERT(n_tokens_all <= cparams.n_batch);
+
+ GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens");
+
+ if (lctx.t_compute_start_us == 0) {
+ lctx.t_compute_start_us = ggml_time_us();
+ }
+ lctx.n_queued_tokens += n_tokens_all;
+
+ auto & kv_self = lctx.kv_self;
+
+ const int64_t n_embd = hparams.n_embd;
+ const int64_t n_vocab = hparams.n_vocab;
+
+ uint32_t n_outputs = 0;
+ uint32_t n_outputs_prev = 0;
+
+ const auto n_ubatch = cparams.n_ubatch;
+
+ // TODO: simplify or deprecate
+ std::vector<llama_pos> pos;
+ std::vector<int32_t> n_seq_id;
+ std::vector<llama_seq_id *> seq_id_arr;
+ std::vector<std::vector<llama_seq_id>> seq_id;
+
+ // this indicates we are doing pooled embedding, so we ignore batch.logits and output all tokens
+ const bool embd_pooled = cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE;
+
+ // count outputs
+ if (batch_all.logits && !embd_pooled) {
+ for (uint32_t i = 0; i < n_tokens_all; ++i) {
+ n_outputs += batch_all.logits[i] != 0;
+ }
+ } else if (lctx.logits_all || embd_pooled) {
+ n_outputs = n_tokens_all;
+ } else {
+ // keep last output only
+ n_outputs = 1;
+ }
+
+ // reserve output buffer
+ if (llama_output_reserve(lctx, n_outputs) < n_outputs) {
+ LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_outputs);
+ return -2;
+ };
+
+ // set output mappings
+ if (batch_all.logits) {
+ int32_t i_logits = 0;
+ for (uint32_t i = 0; i < n_tokens_all; ++i) {
+ if (batch_all.logits[i]) {
+ lctx.output_ids[i] = i_logits++;
+ }
+ }
+ } else {
+ for (uint32_t i = 0; i < n_outputs; ++i) {
+ lctx.output_ids[i] = i;
+ }
+ }
+
+ for (uint32_t cur_token = 0; cur_token < n_tokens_all; cur_token += n_ubatch) {
+ const uint32_t n_tokens = std::min(n_ubatch, n_tokens_all - cur_token);
+ llama_batch u_batch = {
+ /* .n_tokens = */ (int32_t) n_tokens,
+ /* .token = */ batch_all.token ? batch_all.token + cur_token : nullptr,
+ /* .embd = */ batch_all.embd ? batch_all.embd + cur_token*n_embd : nullptr,
+ /* .pos = */ batch_all.pos ? batch_all.pos + cur_token : nullptr,
+ /* .n_seq_id = */ batch_all.n_seq_id ? batch_all.n_seq_id + cur_token : nullptr,
+ /* .seq_id = */ batch_all.seq_id ? batch_all.seq_id + cur_token : nullptr,
+ /* .logits = */ batch_all.logits ? batch_all.logits + cur_token : nullptr,
+ /* .all_pos_0 = */ batch_all.all_pos_0 + (llama_pos) cur_token*batch_all.all_pos_1,
+ /* .all_pos_1 = */ batch_all.all_pos_1,
+ /* .all_seq_id = */ batch_all.all_seq_id,
+ };
+
+ // count the outputs in this u_batch
+ {
+ int32_t n_outputs_new = 0;
+
+ if (u_batch.logits && !embd_pooled) {
+ for (uint32_t i = 0; i < n_tokens; i++) {
+ n_outputs_new += u_batch.logits[i] != 0;
+ }
+ } else if (n_outputs == n_tokens_all) {
+ n_outputs_new = n_tokens;
+ } else {
+ // keep last output only
+ if (cur_token + n_tokens >= n_tokens_all) {
+ n_outputs_new = 1;
+ }
+ }
+
+ // needs to happen before the graph is built
+ lctx.n_outputs = n_outputs_new;
+ }
+
+ int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
+ GGML_ASSERT(n_threads > 0);
+
+ // helpers for smoother batch API transition
+ // after deprecating the llama_eval calls, these will be removed
+ if (u_batch.pos == nullptr) {
+ pos.resize(n_tokens);
+ for (uint32_t i = 0; i < n_tokens; i++) {
+ pos[i] = u_batch.all_pos_0 + i*u_batch.all_pos_1;
+ }
+
+ u_batch.pos = pos.data();
+ }
+
+ if (u_batch.seq_id == nullptr) {
+ n_seq_id.resize(n_tokens);
+ seq_id.resize(n_tokens);
+ seq_id_arr.resize(n_tokens);
+ for (uint32_t i = 0; i < n_tokens; i++) {
+ n_seq_id[i] = 1;
+ seq_id[i].resize(1);
+ seq_id[i][0] = u_batch.all_seq_id;
+ seq_id_arr[i] = seq_id[i].data();
+ }
+
+ u_batch.n_seq_id = n_seq_id.data();
+ u_batch.seq_id = seq_id_arr.data();
+ }
+
+ // non-causal masks do not use the KV cache
+ if (hparams.causal_attn) {
+ llama_kv_cache_update(&lctx);
+
+ // if we have enough unused cells before the current head ->
+ // better to start searching from the beginning of the cache, hoping to fill it
+ if (kv_self.head > kv_self.used + 2*n_tokens) {
+ kv_self.head = 0;
+ }
+
+ if (!llama_kv_cache_find_slot(kv_self, u_batch)) {
+ return 1;
+ }
+
+ if (!kv_self.recurrent) {
+ // a heuristic, to avoid attending the full cache if it is not yet utilized
+ // after enough generations, the benefit from this heuristic disappears
+ // if we start defragmenting the cache, the benefit from this will be more important
+ const uint32_t pad = llama_kv_cache_get_padding(cparams);
+ kv_self.n = std::min(kv_self.size, std::max(pad, GGML_PAD(llama_kv_cache_cell_max(kv_self), pad)));
+ //kv_self.n = llama_kv_cache_cell_max(kv_self);
+ }
+ }
+
+ //printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head);
+
+ ggml_backend_sched_reset(lctx.sched);
+ ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);
+
+ ggml_cgraph * gf = llama_build_graph(lctx, u_batch, false);
+
+ // the output is always the last tensor in the graph
+ struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
+ struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 2];
+
+ if (lctx.n_outputs == 0) {
+ // no output
+ res = nullptr;
+ embd = nullptr;
+ } else if (cparams.embeddings) {
+ res = nullptr; // do not extract logits for embedding case
+ embd = gf->nodes[gf->n_nodes - 1];
+ if (strcmp(embd->name, "result_embd_pooled") != 0) {
+ embd = gf->nodes[gf->n_nodes - 2];
+ }
+ GGML_ASSERT(strcmp(embd->name, "result_embd_pooled") == 0 && "missing embeddings tensor");
+ } else {
+ embd = nullptr; // do not extract embeddings when not needed
+ GGML_ASSERT(strcmp(res->name, "result_output") == 0 && "missing result_output tensor");
+ }
+ // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
+
+ ggml_backend_sched_alloc_graph(lctx.sched, gf);
+
+ llama_set_inputs(lctx, u_batch);
+
+ llama_graph_compute(lctx, gf, n_threads);
+
+ // update the kv ring buffer
+ {
+ kv_self.head += n_tokens;
+
+ // Ensure kv cache head points to a valid index.
+ if (kv_self.head >= kv_self.size) {
+ kv_self.head = 0;
+ }
+ }
+
+ // plot the computation graph in dot format (for debugging purposes)
+ //if (n_past%100 == 0) {
+ // ggml_graph_dump_dot(gf, NULL, "llama.dot");
+ //}
+
+ // extract logits
+ if (res) {
+ ggml_backend_t backend_res = ggml_backend_sched_get_tensor_backend(lctx.sched, res);
+ GGML_ASSERT(backend_res != nullptr);
+ GGML_ASSERT(lctx.logits != nullptr);
+
+ float * logits_out = lctx.logits + n_outputs_prev*n_vocab;
+ const int32_t n_outputs_new = lctx.n_outputs;
+
+ if (n_outputs_new) {
+ GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
+ GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_vocab <= (int64_t) lctx.logits_size);
+ ggml_backend_tensor_get_async(backend_res, res, logits_out, 0, n_outputs_new*n_vocab*sizeof(float));
+ }
+ }
+
+ // extract embeddings
+ if (embd) {
+ ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd);
+ GGML_ASSERT(backend_embd != nullptr);
+
+ switch (cparams.pooling_type) {
+ case LLAMA_POOLING_TYPE_NONE:
+ {
+ // extract token embeddings
+ GGML_ASSERT(lctx.embd != nullptr);
+ float * embd_out = lctx.embd + n_outputs_prev*n_embd;
+ const int32_t n_outputs_new = lctx.n_outputs;
+
+ if (n_outputs_new) {
+ GGML_ASSERT( n_outputs_prev + n_outputs_new <= n_outputs);
+ GGML_ASSERT((n_outputs_prev + n_outputs_new)*n_embd <= (int64_t) lctx.embd_size);
+ ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_outputs_new*n_embd*sizeof(float));
+ }
+ } break;
+ case LLAMA_POOLING_TYPE_MEAN:
+ case LLAMA_POOLING_TYPE_CLS:
+ case LLAMA_POOLING_TYPE_LAST:
+ {
+ // extract sequence embeddings
+ auto & embd_seq_out = lctx.embd_seq;
+ embd_seq_out.clear();
+
+ for (uint32_t i = 0; i < n_tokens; i++) {
+ const llama_seq_id seq_id = u_batch.seq_id[i][0];
+ if (embd_seq_out.find(seq_id) != embd_seq_out.end()) {
+ continue;
+ }
+ embd_seq_out[seq_id].resize(n_embd);
+ ggml_backend_tensor_get_async(backend_embd, embd, embd_seq_out[seq_id].data(), (n_embd*seq_id)*sizeof(float), n_embd*sizeof(float));
+ }
+ } break;
+ case LLAMA_POOLING_TYPE_UNSPECIFIED:
+ {
+ GGML_ASSERT(false && "unknown pooling type");
+ } break;
+ }
+ }
+ n_outputs_prev += lctx.n_outputs;
+ }
+
+ // set to total number of outputs in the batch, for use in llama_get_logits_ith
+ lctx.n_outputs = n_outputs;
+
+ // wait for the computation to finish (automatically done when obtaining the model output)
+ //llama_synchronize(&lctx);
+
+ // decide if we need to defrag the kv cache
+ if (cparams.causal_attn && cparams.defrag_thold >= 0.0f) {
+ const float fragmentation = kv_self.n >= 128 ? 1.0f - float(kv_self.used)/float(kv_self.n) : 0.0f;
+
+ // queue defragmentation for next llama_kv_cache_update
+ if (fragmentation > cparams.defrag_thold) {
+ //LLAMA_LOG_INFO("fragmentation: %.2f\n", fragmentation);
+
+ llama_kv_cache_defrag(kv_self);
+ }
+ }
+
+ // Reset state for the next token before backend sync, to allow the CPU activities in the reset to
+ // overlap with device computation.
+ ggml_backend_sched_reset(lctx.sched);
+
+ return 0;
+}
+
+// encode a batch of tokens by evaluating the encoder part of the transformer
+//
+// - lctx: llama context
+// - batch: batch to evaluate
+//
+// return 0 on success
+// return positive int on warning
+// return negative int on error
+//
+static int llama_encode_internal(
+ llama_context & lctx,
+ llama_batch batch) {
+
+ lctx.is_encoding = true;
+
+ const uint32_t n_tokens = batch.n_tokens;
+
+ if (n_tokens == 0) {
+ LLAMA_LOG_ERROR("%s: n_tokens == 0", __func__);
+ return -1;
+ }
+
+ const auto & model = lctx.model;
+ const auto & hparams = model.hparams;
+ const auto & cparams = lctx.cparams;
+
+ GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
+
+ // micro-batching is not possible for non-causal encoding, so we process the batch in a single shot
+ GGML_ASSERT(cparams.n_ubatch >= n_tokens && "encoder requires n_ubatch >= n_tokens");
+
+ if (lctx.t_compute_start_us == 0) {
+ lctx.t_compute_start_us = ggml_time_us();
+ }
+
+ lctx.n_queued_tokens += n_tokens;
+
+ const int64_t n_embd = hparams.n_embd;
+
+ // TODO: simplify or deprecate
+ std::vector<llama_pos> pos;
+ std::vector<int32_t> n_seq_id;
+ std::vector<llama_seq_id *> seq_id_arr;
+ std::vector<std::vector<llama_seq_id>> seq_id;
+
+ // reserve output buffer
+ if (llama_output_reserve(lctx, n_tokens) < n_tokens) {
+ LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_tokens);
+ return -2;
+ };
+
+ for (uint32_t i = 0; i < n_tokens; ++i) {
+ lctx.output_ids[i] = i;
+ }
+
+ lctx.inp_embd_enc = NULL;
+ lctx.n_outputs = n_tokens;
+
+ const int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
+ GGML_ASSERT(n_threads > 0);
+
+ // helpers for smoother batch API transition
+ // after deprecating the llama_eval calls, these will be removed
+ if (batch.pos == nullptr) {
+ pos.resize(n_tokens);
+ for (uint32_t i = 0; i < n_tokens; i++) {
+ pos[i] = batch.all_pos_0 + i*batch.all_pos_1;
+ }
+
+ batch.pos = pos.data();
+ }
+
+ if (batch.seq_id == nullptr) {
+ n_seq_id.resize(n_tokens);
+ seq_id.resize(n_tokens);
+ seq_id_arr.resize(n_tokens);
+ for (uint32_t i = 0; i < n_tokens; i++) {
+ n_seq_id[i] = 1;
+ seq_id[i].resize(1);
+ seq_id[i][0] = batch.all_seq_id;
+ seq_id_arr[i] = seq_id[i].data();
+ }
+
+ batch.n_seq_id = n_seq_id.data();
+ batch.seq_id = seq_id_arr.data();
+ }
+
+ ggml_backend_sched_reset(lctx.sched);
+ ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);
+
+ ggml_cgraph * gf = llama_build_graph(lctx, batch, false);
+
+ // the output embeddings after the final encoder normalization
+ struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 1];
+
+ GGML_ASSERT(strcmp(embd->name, "result_norm") == 0);
+
+ ggml_backend_sched_alloc_graph(lctx.sched, gf);
+
+ llama_set_inputs(lctx, batch);
+
+ llama_graph_compute(lctx, gf, n_threads);
+
+ // extract embeddings
+ if (embd) {
+ ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd);
+ GGML_ASSERT(backend_embd != nullptr);
+
+ // extract token embeddings
+ GGML_ASSERT(lctx.embd != nullptr);
+
+ lctx.embd_enc.resize(n_tokens*n_embd);
+ float * embd_out = lctx.embd_enc.data();
+
+ ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_tokens*n_embd*sizeof(float));
+
+ // remember the sequence ids used during the encoding - needed for cross attention later
+ lctx.seq_ids_enc.resize(n_tokens);
+ for (uint32_t i = 0; i < n_tokens; i++) {
+ for (int s = 0; s < batch.n_seq_id[i]; s++) {
+ llama_seq_id seq_id = batch.seq_id[i][s];
+ lctx.seq_ids_enc[i].insert(seq_id);
+ }
+ }
+ }
+
+ // Reset state for the next token before backend sync, to allow the CPU activities in the reset to
+ // overlap with device computation.
+ ggml_backend_sched_reset(lctx.sched);
+
+ return 0;
+}
+
+// find holes from the beginning of the KV cache and fill them by moving data from the end of the cache
+static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
+ auto & kv_self = lctx.kv_self;
+
+ const auto & hparams = lctx.model.hparams;
+
+ const uint32_t n_layer = hparams.n_layer;
+
+ const uint32_t n_kv = llama_kv_cache_cell_max(kv_self);
+ const uint32_t n_used = kv_self.used;
+
+ assert(n_used <= n_kv);
+
+ //const int64_t t_start = ggml_time_us();
+
+ // number of cells moved
+ uint32_t n_moves = 0;
+
+ // each move requires 6*n_layer tensors (see build_defrag)
+ // - source view, destination view, copy operation
+ // - x2 for keys and values
+ //const uint32_t max_moves = LLAMA_MAX_NODES/(6*n_layer);
+ // TODO: tmp fix https://github.com/ggerganov/llama.cpp/issues/6685#issuecomment-2057579516
+ const uint32_t max_moves = (LLAMA_MAX_NODES - 2*n_layer)/(6*n_layer);
+
+ // determine which KV cells to move where
+ //
+ // cell i moves to ids[i]
+ //
+ // if ids[i] == i || ids[i] == n_kv, then cell i is not moved
+ //
+ std::vector<uint32_t> ids(n_kv, n_kv);
+
+ for (uint32_t i0 = 0; i0 < n_used; ++i0) {
+ const auto & cell0 = kv_self.cells[i0];
+
+ if (!cell0.is_empty()) {
+ ids[i0] = i0;
+
+ continue;
+ }
+
+ // found a hole - fill it with data from the end of the cache
+
+ uint32_t nh = 1;
+
+ // determine the size of the hole
+ while (i0 + nh < n_used && kv_self.cells[i0 + nh].is_empty()) {
+ nh++;
+ }
+
+ uint32_t nf = 0;
+ uint32_t is = n_kv - 1;
+
+ // starting from the end, find nh non-empty cells
+ for (; is > i0; --is) {
+ const auto & cell1 = kv_self.cells[is];
+
+ if (cell1.is_empty() || ids[is] != n_kv) {
+ continue;
+ }
+
+ // non-empty cell which is not yet moved
+ nf++;
+
+ if (nf == nh) {
+ break;
+ }
+ }
+
+ // this can only happen if `n_used` is not accurate, which would be a bug
+ GGML_ASSERT(nf == nh && "KV defrag bug: nf != nh");
+
+ nf = 0;
+
+ uint32_t i1 = is;
+
+ // are we moving a continuous block of memory?
+ bool cont = false;
+
+ // should we stop searching for the next move?
+ bool stop = false;
+
+ // go back and move the nf cells to the hole
+ for (; i1 < n_kv; ++i1) {
+ auto & cell1 = kv_self.cells[i1];
+
+ if (cell1.is_empty() || ids[i1] != n_kv) {
+ if (n_moves == max_moves) {
+ stop = true;
+ break;
+ }
+
+ cont = false;
+ continue;
+ }
+
+ // this cell goes to (i0 + nf)
+ ids[i1] = i0 + nf;
+
+ // move the cell meta data
+ kv_self.cells[i0 + nf] = cell1;
+
+ // clear the old cell and move the head there
+ cell1 = llama_kv_cell();
+ kv_self.head = n_used;
+
+ if (!cont) {
+ n_moves++;
+ cont = true;
+ }
+
+ nf++;
+
+ if (nf == nh) {
+ break;
+ }
+ }
+
+ if (stop || n_moves == max_moves) {
+ break;
+ }
+
+ //LLAMA_LOG_INFO("(tmp log) KV defrag: move [%u, %u) to [%u, %u)\n", is, i1 + 1, i0, i0 + nh);
+
+ i0 += nh - 1;
+ }
+
+ if (n_moves == 0) {
+ return;
+ }
+
+ //LLAMA_LOG_INFO("(tmp log) KV defrag cell moves: %u\n", n_moves);
+
+ //LLAMA_LOG_INFO("expected gf nodes: %u\n", 6*n_moves*n_layer);
+
+#if 0
+ // CPU defrag
+ //
+ // TODO: optimizations are possible:
+ // - multiple threads
+ // - avoid copying to the host memory when already there
+ //
+ // likely not worth the effort, as we have ggml_graph based defrag
+ //
+
+ const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa();
+ const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
+
+ const uint32_t kv_size = kv_self.size;
+
+ std::vector<uint8_t> buf_k;
+ std::vector<uint8_t> buf_v;
+
+ for (uint32_t il = 0; il < n_layer; ++il) {
+ const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
+ const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_size);
+
+ const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
+ const size_t v_size = ggml_row_size (kv_self.v_l[il]->type, n_embd_v_gqa*kv_size);
+
+ buf_k.resize(k_size);
+ buf_v.resize(v_size);
+
+ ggml_backend_tensor_get(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
+ ggml_backend_tensor_get(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());
+
+ // batch move [i, i+nm) to [id, id+nm)
+ // note: cells can move only to a lower index
+ for (uint32_t i = 0; i < n_kv; ++i) {
+ const uint32_t id = ids[i];
+
+ if (i == id || id == n_kv) {
+ continue;
+ }
+
+ uint32_t nm = 1;
+
+ while (i + nm < n_kv && ids[i + nm] == id + nm) {
+ nm++;
+ }
+
+ // move keys
+ {
+ const int64_t os = i*k_size_row;
+ const int64_t od = id*k_size_row;
+
+ memcpy(buf_k.data() + od, buf_k.data() + os, nm*k_size_row);
+ }
+
+ // move values (note: they are transposed)
+ {
+ const int64_t os = i;
+ const int64_t od = id;
+
+ for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+ memcpy(buf_v.data() + (od + j*kv_size)*v_size_el, buf_v.data() + (os + j*kv_size)*v_size_el, nm*v_size_el);
+ }
+ }
+
+ i += nm - 1;
+ }
+
+ ggml_backend_tensor_set(kv_self.k_l[il], buf_k.data(), 0, buf_k.size());
+ ggml_backend_tensor_set(kv_self.v_l[il], buf_v.data(), 0, buf_v.size());
+ }
+#else
+ // ggml_graph defrag
+
+ ggml_backend_sched_reset(lctx.sched);
+
+ ggml_cgraph * gf = llama_build_graph_defrag(lctx, ids);
+
+ llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
+#endif
+
+ //const int64_t t_end = ggml_time_us();
+
+ //LLAMA_LOG_INFO("(tmp log) KV defrag time: %.3f ms\n", (t_end - t_start)/1000.0);
+}
+
+static void llama_kv_cache_update_internal(struct llama_context & lctx) {
+ bool need_reserve = false;
+
+ // apply K-shift if needed
+ if (lctx.model.hparams.rope_type != LLAMA_ROPE_TYPE_NONE && lctx.kv_self.has_shift) {
+ if (lctx.model.arch == LLM_ARCH_DEEPSEEK2) { // not supported due to MLA
+ GGML_ASSERT(false && "Deepseek2 does not support K-shift");
+ }
+
+ {
+ ggml_backend_sched_reset(lctx.sched);
+
+ ggml_cgraph * gf = llama_build_graph_k_shift(lctx);
+
+ ggml_backend_sched_alloc_graph(lctx.sched, gf);
+
+ llama_set_k_shift(lctx);
+
+ llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
+
+ need_reserve = true;
+ }
+
+ {
+ auto & kv_self = lctx.kv_self;
+
+ kv_self.has_shift = false;
+
+ for (uint32_t i = 0; i < kv_self.size; ++i) {
+ kv_self.cells[i].delta = 0;
+ }
+ }
+ }
+
+ if (lctx.kv_self.recurrent && lctx.kv_self.do_copy) {
+ {
+ ggml_backend_sched_reset(lctx.sched);
+
+ ggml_cgraph * gf = llama_build_graph_s_copy(lctx);
+
+ ggml_backend_sched_alloc_graph(lctx.sched, gf);
+
+ llama_set_s_copy(lctx);
+
+ llama_graph_compute(lctx, gf, lctx.cparams.n_threads);
+
+ need_reserve = true;
+ }
+
+ {
+ auto & kv_self = lctx.kv_self;
+
+ kv_self.do_copy = false;
+
+ for (uint32_t i = 0; i < kv_self.size; ++i) {
+ kv_self.cells[i].src = i;
+ }
+ }
+ }
+
+ // defragment the KV cache if needed
+ if (lctx.kv_self.do_defrag) {
+ llama_kv_cache_defrag_internal(lctx);
+
+ need_reserve = true;
+
+ lctx.kv_self.do_defrag = false;
+ }
+
+ // reserve a worst case graph again
+ if (need_reserve) {
+ // TODO: extract to a function
+ // build worst-case graph
+ int n_tokens = (int)std::min(lctx.cparams.n_ctx, lctx.cparams.n_ubatch);
+ int n_past = lctx.cparams.n_ctx - n_tokens;
+ llama_token token = llama_token_bos(&lctx.model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
+ ggml_cgraph * gf = llama_build_graph(lctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true);
+
+ // initialize scheduler with the worst-case graph
+ ggml_backend_sched_reset(lctx.sched);
+ if (!ggml_backend_sched_reserve(lctx.sched, gf)) {
+ LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
+ }
+ }
+}
+
+//
+// quantization
+//
+
+struct quantize_state_internal {
+ const llama_model & model;
+ const llama_model_quantize_params * params;
+
+ int n_attention_wv = 0;
+ int n_ffn_down = 0;
+ int n_ffn_gate = 0;
+ int n_ffn_up = 0;
+ int i_attention_wv = 0;
+ int i_ffn_down = 0;
+ int i_ffn_gate = 0;
+ int i_ffn_up = 0;
+
+ int n_k_quantized = 0;
+ int n_fallback = 0;
+
+ bool has_imatrix = false;
+
+ // used to figure out if a model shares tok_embd with the output weight
+ bool has_output = false;
+
+ quantize_state_internal(const llama_model & model, const llama_model_quantize_params * params)
+ : model(model)
+ , params(params)
+ {}
+};
+
+static void llama_tensor_dequantize_internal(
+ struct ggml_tensor * tensor, std::vector<no_init<float>> & output, std::vector<std::thread> & workers,
+ const size_t nelements, const int nthread
+) {
+ if (output.size() < nelements) {
+ output.resize(nelements);
+ }
+ float * f32_output = (float *) output.data();
+
+ ggml_type_traits_t qtype;
+ if (ggml_is_quantized(tensor->type)) {
+ qtype = ggml_internal_get_type_traits(tensor->type);
+ if (qtype.to_float == NULL) {
+ throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor->type)));
+ }
+ } else if (tensor->type != GGML_TYPE_F16 &&
+ tensor->type != GGML_TYPE_BF16) {
+ throw std::runtime_error(format("cannot dequantize/convert tensor type %s", ggml_type_name(tensor->type)));
+ }
+
+ if (nthread < 2) {
+ if (tensor->type == GGML_TYPE_F16) {
+ ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor->data, f32_output, nelements);
+ } else if (tensor->type == GGML_TYPE_BF16) {
+ ggml_bf16_to_fp32_row((ggml_bf16_t *)tensor->data, f32_output, nelements);
+ } else if (ggml_is_quantized(tensor->type)) {
+ qtype.to_float(tensor->data, f32_output, nelements);
+ } else {
+ GGML_ASSERT(false); // unreachable
+ }
+ return;
+ }
+
+ size_t block_size;
+ if (tensor->type == GGML_TYPE_F16 ||
+ tensor->type == GGML_TYPE_BF16) {
+ block_size = 1;
+ } else {
+ block_size = (size_t)ggml_blck_size(tensor->type);
+ }
+
+ size_t block_size_bytes = ggml_type_size(tensor->type);
+
+ GGML_ASSERT(nelements % block_size == 0);
+ size_t nblocks = nelements / block_size;
+ size_t blocks_per_thread = nblocks / nthread;
+ size_t spare_blocks = nblocks - (blocks_per_thread * nthread); // if blocks aren't divisible by thread count
+
+ size_t in_buff_offs = 0;
+ size_t out_buff_offs = 0;
+
+ for (int tnum = 0; tnum < nthread; tnum++) {
+ size_t thr_blocks = blocks_per_thread + (tnum == nthread - 1 ? spare_blocks : 0); // num blocks for this thread
+ size_t thr_elems = thr_blocks * block_size; // number of elements for this thread
+ size_t thr_block_bytes = thr_blocks * block_size_bytes; // number of input bytes for this thread
+
+ auto compute = [qtype] (ggml_type typ, uint8_t * inbuf, float * outbuf, int nels) {
+ if (typ == GGML_TYPE_F16) {
+ ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels);
+ } else if (typ == GGML_TYPE_BF16) {
+ ggml_bf16_to_fp32_row((ggml_bf16_t *)inbuf, outbuf, nels);
+ } else {
+ qtype.to_float(inbuf, outbuf, nels);
+ }
+ };
+ workers.emplace_back(compute, tensor->type, (uint8_t *) tensor->data + in_buff_offs, f32_output + out_buff_offs, thr_elems);
+ in_buff_offs += thr_block_bytes;
+ out_buff_offs += thr_elems;
+ }
+ for (auto & w : workers) { w.join(); }
+ workers.clear();
+}
+
+static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type new_type, const ggml_tensor * tensor, llama_ftype ftype) {
+ const std::string name = ggml_get_name(tensor);
+
+ // TODO: avoid hardcoded tensor names - use the TN_* constants
+ const llm_arch arch = qs.model.arch;
+ const auto tn = LLM_TN(arch);
+
+ auto use_more_bits = [](int i_layer, int n_layers) -> bool {
+ return i_layer < n_layers/8 || i_layer >= 7*n_layers/8 || (i_layer - n_layers/8)%3 == 2;
+ };
+ const int n_expert = std::max(1, (int)qs.model.hparams.n_expert);
+ auto layer_info = [n_expert] (int i_layer, int n_layer, const char * name) {
+ if (n_expert > 1) {
+ // Believe it or not, "experts" in the FFN of Mixtral-8x7B are not consecutive, but iccasionally randomly
+ // sprinkled in the model. Hence, simply dividing i_ffn_down by n_expert does not work
+ // for getting the current layer as I initially thought, and we need to resort to parsing the
+ // tensor name.
+ if (sscanf(name, "blk.%d.", &i_layer) != 1) {
+ throw std::runtime_error(format("Failed to determine layer for tensor %s", name));
+ }
+ if (i_layer < 0 || i_layer >= n_layer) {
+ throw std::runtime_error(format("Bad layer %d for tensor %s. Must be in [0, %d)", i_layer, name, n_layer));
+ }
+ }
+ return std::make_pair(i_layer, n_layer);
+ };
+
+ // for arches that share the same tensor between the token embeddings and the output, we quantize the token embeddings
+ // with the quantization of the output tensor
+ if (name == tn(LLM_TENSOR_OUTPUT, "weight") || (!qs.has_output && name == tn(LLM_TENSOR_TOKEN_EMBD, "weight"))) {
+ if (qs.params->output_tensor_type < GGML_TYPE_COUNT) {
+ new_type = qs.params->output_tensor_type;
+ } else {
+ int nx = tensor->ne[0];
+ if (arch == LLM_ARCH_FALCON || nx % QK_K != 0) {
+ new_type = GGML_TYPE_Q8_0;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
+ ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ||
+ ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
+ new_type = GGML_TYPE_Q5_K;
+ }
+ else if (new_type != GGML_TYPE_Q8_0) {
+ new_type = GGML_TYPE_Q6_K;
+ }
+ }
+ } else if (name == "token_embd.weight") {
+ if (qs.params->token_embedding_type < GGML_TYPE_COUNT) {
+ new_type = qs.params->token_embedding_type;
+ } else {
+ if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS ||
+ ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
+ new_type = GGML_TYPE_Q2_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) {
+ new_type = GGML_TYPE_IQ3_S;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+ new_type = GGML_TYPE_IQ3_S;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_BN || ftype == LLAMA_FTYPE_MOSTLY_IQ2_BN) {
+ new_type = GGML_TYPE_IQ4_NL;
+ }
+ else if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8 ||
+ new_type == GGML_TYPE_Q4_0_8_8) {
+ new_type = GGML_TYPE_Q4_0;
+ }
+ }
+ } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
+ ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) {
+ if (name.find("attn_v.weight") != std::string::npos) {
+ if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K;
+ else new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
+ ++qs.i_attention_wv;
+ }
+ else if (qs.model.hparams.n_expert == 8 && name.find("attn_k.weight") != std::string::npos) {
+ new_type = GGML_TYPE_Q4_K;
+ }
+ else if (name.find("ffn_down") != std::string::npos) {
+ if (qs.i_ffn_down < qs.n_ffn_down/8) {
+ new_type = ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M ? GGML_TYPE_IQ3_S : GGML_TYPE_Q2_K;
+ }
+ ++qs.i_ffn_down;
+ }
+ else if (name.find("attn_output.weight") != std::string::npos) {
+ if (qs.model.hparams.n_expert == 8) {
+ new_type = GGML_TYPE_Q5_K;
+ } else {
+ if (ftype == LLAMA_FTYPE_MOSTLY_IQ1_S || ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) new_type = GGML_TYPE_IQ2_XXS;
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_S || ftype == LLAMA_FTYPE_MOSTLY_IQ2_M) new_type = GGML_TYPE_IQ3_S;
+ }
+ }
+ } else if (name.find("attn_v.weight") != std::string::npos) {
+ if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) {
+ new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) {
+ new_type = GGML_TYPE_Q4_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+ new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_IQ3_S : GGML_TYPE_IQ3_XXS;
+ }
+ else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S) && qs.model.hparams.n_gqa() >= 4) {
+ new_type = GGML_TYPE_Q4_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
+ new_type = GGML_TYPE_Q4_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
+ new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K;
+ else if ((ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && qs.model.hparams.n_gqa() >= 4) {
+ new_type = GGML_TYPE_Q5_K;
+ }
+ else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) &&
+ use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K;
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K;
+ if (qs.model.type == MODEL_70B) {
+ // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is
+ // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with
+ // nearly negligible increase in model size by quantizing this tensor with more bits:
+ if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K;
+ }
+ if (qs.model.hparams.n_expert == 8) {
+ // for the 8-expert model, bumping this to Q8_0 trades just ~128MB
+ // TODO: explore better strategies
+ new_type = GGML_TYPE_Q8_0;
+ }
+ ++qs.i_attention_wv;
+ } else if (name.find("attn_k.weight") != std::string::npos) {
+ if (qs.model.hparams.n_expert == 8) {
+ // for the 8-expert model, bumping this to Q8_0 trades just ~128MB
+ // TODO: explore better strategies
+ new_type = GGML_TYPE_Q8_0;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
+ new_type = GGML_TYPE_IQ3_XXS;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+ new_type = GGML_TYPE_IQ2_S;
+ }
+ } else if (name.find("attn_q.weight") != std::string::npos) {
+ if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS) {
+ new_type = GGML_TYPE_IQ3_XXS;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) {
+ new_type = GGML_TYPE_IQ2_S;
+ }
+ } else if (name.find("ffn_down") != std::string::npos) {
+ auto info = layer_info(qs.i_ffn_down, qs.n_ffn_down, name.c_str());
+ int i_layer = info.first, n_layer = info.second;
+ if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S) {
+ if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) {
+ new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) {
+ new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K
+ : arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K
+ : GGML_TYPE_Q3_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M && (i_layer < n_layer/8 ||
+ (qs.model.hparams.n_expert == 8 && use_more_bits(i_layer, n_layer)))) {
+ new_type = GGML_TYPE_Q4_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) {
+ new_type = arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) {
+ if (arch == LLM_ARCH_FALCON) {
+ new_type = i_layer < n_layer/16 ? GGML_TYPE_Q6_K :
+ use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K;
+ } else {
+ if (use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
+ }
+ }
+ else if (i_layer < n_layer/8 && (ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) && !qs.has_imatrix) {
+ new_type = GGML_TYPE_Q5_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_layer, n_layer)) new_type = GGML_TYPE_Q6_K;
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && arch != LLM_ARCH_FALCON && i_layer < n_layer/8) {
+ new_type = GGML_TYPE_Q5_K;
+ }
+ else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_0 || ftype == LLAMA_FTYPE_MOSTLY_Q5_0)
+ && qs.has_imatrix && i_layer < n_layer/8) {
+ // Guard against craziness in the first few ffn_down layers that can happen even with imatrix for Q4_0/Q5_0.
+ // We only do it when an imatrix is provided because a) we want to make sure that one can always get the
+ // same quantization as before imatrix stuff, and b) Q4_1/Q5_1 do go crazy on ffn_down without an imatrix.
+ new_type = ftype == LLAMA_FTYPE_MOSTLY_Q4_0 ? GGML_TYPE_Q4_1 : GGML_TYPE_Q5_1;
+ }
+ ++qs.i_ffn_down;
+ } else if (name.find("attn_output.weight") != std::string::npos) {
+ if (arch != LLM_ARCH_FALCON) {
+ if (qs.model.hparams.n_expert == 8) {
+ if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS ||
+ ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_NL ||
+ ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_IQ3_S ||
+ ftype == LLAMA_FTYPE_MOSTLY_IQ3_M || ftype == LLAMA_FTYPE_MOSTLY_IQ4_XS) {
+ new_type = GGML_TYPE_Q5_K;
+ }
+ } else {
+ if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K;
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) new_type = GGML_TYPE_IQ3_S;
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M ) new_type = GGML_TYPE_Q4_K;
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L ) new_type = GGML_TYPE_Q5_K;
+ else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_M ) new_type = GGML_TYPE_Q4_K;
+ }
+ } else {
+ if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K;
+ }
+ }
+ else if (name.find("attn_qkv.weight") != std::string::npos) {
+ if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L || ftype == LLAMA_FTYPE_MOSTLY_IQ3_M) {
+ new_type = GGML_TYPE_Q4_K;
+ }
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K;
+ else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K;
+ }
+ else if (name.find("ffn_gate") != std::string::npos) {
+ auto info = layer_info(qs.i_ffn_gate, qs.n_ffn_gate, name.c_str());
+ int i_layer = info.first, n_layer = info.second;
+ if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
+ new_type = GGML_TYPE_IQ3_XXS;
+ }
+ ++qs.i_ffn_gate;
+ }
+ else if (name.find("ffn_up") != std::string::npos) {
+ auto info = layer_info(qs.i_ffn_up, qs.n_ffn_up, name.c_str());
+ int i_layer = info.first, n_layer = info.second;
+ if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XS && (i_layer >= n_layer/8 && i_layer < 7*n_layer/8)) {
+ new_type = GGML_TYPE_IQ3_XXS;
+ }
+ ++qs.i_ffn_up;
+ }
+
+ // if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
+ //}
+ // IK: let's remove this, else Q2_K is almost the same as Q3_K_S
+ //else if (name.find("ffn_gate") != std::string::npos || name.find("ffn_up") != std::string::npos) {
+ // if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K;
+ //}
+ // This can be used to reduce the size of the Q5_K_S model.
+ // The associated PPL increase is fully in line with the size reduction
+ //else {
+ // if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_S) new_type = GGML_TYPE_Q4_K;
+ //}
+ bool convert_incompatible_tensor = false;
+ if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K ||
+ new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || new_type == GGML_TYPE_IQ4_XS ||
+ new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || new_type == GGML_TYPE_IQ2_S ||
+ new_type == GGML_TYPE_IQ3_XXS || new_type == GGML_TYPE_IQ1_S || new_type == GGML_TYPE_IQ3_S ||
+ new_type == GGML_TYPE_IQ1_M) {
+ int nx = tensor->ne[0];
+ int ny = tensor->ne[1];
+ if (nx % QK_K != 0) {
+ LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for %s", __func__, nx, ny, QK_K, ggml_type_name(new_type));
+ convert_incompatible_tensor = true;
+ } else {
+ ++qs.n_k_quantized;
+ }
+ }
+ if (new_type == GGML_TYPE_IQ1_BN || new_type == GGML_TYPE_IQ2_BN) {
+ int nx = tensor->ne[0];
+ if (nx % QK_IQ1BN != 0) {
+ convert_incompatible_tensor = true;
+ }
+ }
+ if (convert_incompatible_tensor) {
+ switch (new_type) {
+ case GGML_TYPE_IQ2_XXS:
+ case GGML_TYPE_IQ2_XS:
+ case GGML_TYPE_IQ2_S:
+ case GGML_TYPE_IQ3_XXS:
+ case GGML_TYPE_IQ3_S:
+ case GGML_TYPE_IQ1_S:
+ case GGML_TYPE_IQ1_M:
+ case GGML_TYPE_Q2_K:
+ case GGML_TYPE_Q3_K:
+ case GGML_TYPE_IQ4_XS: new_type = GGML_TYPE_IQ4_NL; break;
+ case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break;
+ case GGML_TYPE_Q5_K: new_type = GGML_TYPE_Q5_1; break;
+ case GGML_TYPE_Q6_K: new_type = GGML_TYPE_Q8_0; break;
+ default: throw std::runtime_error("\nUnsupported tensor size encountered\n");
+ }
+ LLAMA_LOG_WARN(" - using fallback quantization %s\n", ggml_type_name(new_type));
+ ++qs.n_fallback;
+ }
+
+ return new_type;
+}
+
+static size_t llama_tensor_quantize_internal(enum ggml_type new_type, const float * f32_data, void * new_data, const int64_t chunk_size, int64_t nrows, int64_t n_per_row, const float * imatrix, std::vector<std::thread> & workers, const int nthread) {
+ if (nthread < 2) {
+ // single-thread
+ size_t new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nrows, n_per_row, imatrix);
+ if (!ggml_validate_row_data(new_type, new_data, new_size)) {
+ throw std::runtime_error("quantized data validation failed");
+ }
+ return new_size;
+ }
+
+ std::mutex mutex;
+ int64_t counter = 0;
+ size_t new_size = 0;
+ bool valid = true;
+ auto compute = [&mutex, &counter, &new_size, &valid, new_type, f32_data, new_data, chunk_size,
+ nrows, n_per_row, imatrix]() {
+ const int64_t nrows_per_chunk = chunk_size / n_per_row;
+ size_t local_size = 0;
+ while (true) {
+ std::unique_lock<std::mutex> lock(mutex);
+ int64_t first_row = counter; counter += nrows_per_chunk;
+ if (first_row >= nrows) {
+ if (local_size > 0) {
+ new_size += local_size;
+ }
+ break;
+ }
+ lock.unlock();
+ const int64_t this_nrow = std::min(nrows - first_row, nrows_per_chunk);
+ size_t this_size = ggml_quantize_chunk(new_type, f32_data, new_data, first_row * n_per_row, this_nrow, n_per_row, imatrix);
+ local_size += this_size;
+
+ // validate the quantized data
+ const size_t row_size = ggml_row_size(new_type, n_per_row);
+ void * this_data = (char *) new_data + first_row * row_size;
+ if (!ggml_validate_row_data(new_type, this_data, this_size)) {
+ std::unique_lock<std::mutex> lock(mutex);
+ valid = false;
+ break;
+ }
+ }
+ };
+ for (int it = 0; it < nthread - 1; ++it) {
+ workers.emplace_back(compute);
+ }
+ compute();
+ for (auto & w : workers) { w.join(); }
+ workers.clear();
+ if (!valid) {
+ throw std::runtime_error("quantized data validation failed");
+ }
+ return new_size;
+}
+
+static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, const llama_model_quantize_params * params) {
+ ggml_type default_type;
+ llama_ftype ftype = params->ftype;
+
+ switch (params->ftype) {
+ case LLAMA_FTYPE_MOSTLY_Q4_0: default_type = GGML_TYPE_Q4_0; break;
+ case LLAMA_FTYPE_MOSTLY_Q4_1: default_type = GGML_TYPE_Q4_1; break;
+ case LLAMA_FTYPE_MOSTLY_Q5_0: default_type = GGML_TYPE_Q5_0; break;
+ case LLAMA_FTYPE_MOSTLY_Q5_1: default_type = GGML_TYPE_Q5_1; break;
+ case LLAMA_FTYPE_MOSTLY_Q8_0: default_type = GGML_TYPE_Q8_0; break;
+ case LLAMA_FTYPE_MOSTLY_F16: default_type = GGML_TYPE_F16; break;
+ case LLAMA_FTYPE_MOSTLY_BF16: default_type = GGML_TYPE_BF16; break;
+ case LLAMA_FTYPE_ALL_F32: default_type = GGML_TYPE_F32; break;
+
+ // K-quants
+ case LLAMA_FTYPE_MOSTLY_Q2_K_S:
+ case LLAMA_FTYPE_MOSTLY_Q2_K: default_type = GGML_TYPE_Q2_K; break;
+ case LLAMA_FTYPE_MOSTLY_IQ3_XS: default_type = GGML_TYPE_IQ3_S; break;
+ case LLAMA_FTYPE_MOSTLY_Q3_K_S:
+ case LLAMA_FTYPE_MOSTLY_Q3_K_M:
+ case LLAMA_FTYPE_MOSTLY_Q3_K_L: default_type = GGML_TYPE_Q3_K; break;
+ case LLAMA_FTYPE_MOSTLY_Q4_K_S:
+ case LLAMA_FTYPE_MOSTLY_Q4_K_M: default_type = GGML_TYPE_Q4_K; break;
+ case LLAMA_FTYPE_MOSTLY_Q5_K_S:
+ case LLAMA_FTYPE_MOSTLY_Q5_K_M: default_type = GGML_TYPE_Q5_K; break;
+ case LLAMA_FTYPE_MOSTLY_Q6_K: default_type = GGML_TYPE_Q6_K; break;
+ case LLAMA_FTYPE_MOSTLY_IQ2_XXS: default_type = GGML_TYPE_IQ2_XXS; break;
+ case LLAMA_FTYPE_MOSTLY_IQ2_XS: default_type = GGML_TYPE_IQ2_XS; break;
+ case LLAMA_FTYPE_MOSTLY_IQ2_S: default_type = GGML_TYPE_IQ2_XS; break;
+ case LLAMA_FTYPE_MOSTLY_IQ2_M: default_type = GGML_TYPE_IQ2_S; break;
+ case LLAMA_FTYPE_MOSTLY_IQ3_XXS: default_type = GGML_TYPE_IQ3_XXS; break;
+ case LLAMA_FTYPE_MOSTLY_IQ1_S: default_type = GGML_TYPE_IQ1_S; break;
+ case LLAMA_FTYPE_MOSTLY_IQ1_M: default_type = GGML_TYPE_IQ1_M; break;
+ case LLAMA_FTYPE_MOSTLY_IQ1_BN: default_type = GGML_TYPE_IQ1_BN; break;
+ case LLAMA_FTYPE_MOSTLY_IQ2_BN: default_type = GGML_TYPE_IQ2_BN; break;
+ case LLAMA_FTYPE_MOSTLY_IQ4_NL: default_type = GGML_TYPE_IQ4_NL; break;
+ case LLAMA_FTYPE_MOSTLY_IQ4_XS: default_type = GGML_TYPE_IQ4_XS; break;
+ case LLAMA_FTYPE_MOSTLY_IQ3_S: default_type = GGML_TYPE_IQ3_S; break;
+ case LLAMA_FTYPE_MOSTLY_IQ3_M: default_type = GGML_TYPE_IQ3_S; break;
+ case LLAMA_FTYPE_MOSTLY_Q4_0_4_4: default_type = GGML_TYPE_Q4_0_4_4; break;
+ case LLAMA_FTYPE_MOSTLY_Q4_0_4_8: default_type = GGML_TYPE_Q4_0_4_8; break;
+ case LLAMA_FTYPE_MOSTLY_Q4_0_8_8: default_type = GGML_TYPE_Q4_0_8_8; break;
+
+ default: throw std::runtime_error(format("invalid output file type %d\n", ftype));
+ }
+
+ int nthread = params->nthread;
+
+ if (nthread <= 0) {
+ nthread = std::thread::hardware_concurrency();
+ }
+
+ // mmap consistently increases speed Linux, and also increases speed on Windows with
+ // hot cache. It may cause a slowdown on macOS, possibly related to free memory.
+#if defined(__linux__) || defined(_WIN32)
+ constexpr bool use_mmap = true;
+#else
+ constexpr bool use_mmap = false;
+#endif
+
+ llama_model_kv_override * kv_overrides = nullptr;
+ if (params->kv_overrides) {
+ auto v = (std::vector<llama_model_kv_override>*)params->kv_overrides;
+ kv_overrides = v->data();
+ }
+ llama_model_loader ml(fname_inp, use_mmap, /*check_tensors*/ true, kv_overrides);
+ ml.init_mappings(false); // no prefetching
+
+ llama_model model;
+ llm_load_arch(ml, model);
+ llm_load_hparams(ml, model);
+
+ struct quantize_state_internal qs(model, params);
+
+ if (params->only_copy) {
+ ftype = model.ftype;
+ }
+ const std::unordered_map<std::string, std::vector<float>> * imatrix_data = nullptr;
+ if (params->imatrix) {
+ imatrix_data = static_cast<const std::unordered_map<std::string, std::vector<float>>*>(params->imatrix);
+ if (imatrix_data) {
+ LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(imatrix_data->size()));
+ qs.has_imatrix = true;
+ // check imatrix for nans or infs
+ for (const auto & kv : *imatrix_data) {
+ for (float f : kv.second) {
+ if (!std::isfinite(f)) {
+ throw std::runtime_error(format("imatrix contains non-finite value %f\n", f));
+ }
+ }
+ }
+ }
+ }
+
+ const size_t align = GGUF_DEFAULT_ALIGNMENT;
+ struct gguf_context * ctx_out = gguf_init_empty();
+
+ // copy the KV pairs from the input file
+ gguf_set_kv (ctx_out, ml.meta);
+ gguf_set_val_u32(ctx_out, "general.quantization_version", GGML_QNT_VERSION); // TODO: use LLM_KV
+ gguf_set_val_u32(ctx_out, "general.file_type", ftype); // TODO: use LLM_KV
+
+ // Remove split metadata
+ gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_NO).c_str());
+ gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str());
+ gguf_remove_key(ctx_out, ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str());
+
+ if (params->kv_overrides) {
+ const std::vector<llama_model_kv_override> & overrides = *(const std::vector<llama_model_kv_override> *)params->kv_overrides;
+ for (auto & o : overrides) {
+ if (o.key[0] == 0) break;
+ if (o.tag == LLAMA_KV_OVERRIDE_TYPE_FLOAT) {
+ gguf_set_val_f32(ctx_out, o.key, o.val_f64);
+ } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_INT) {
+ gguf_set_val_i32(ctx_out, o.key, o.val_i64);
+ } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_BOOL) {
+ gguf_set_val_bool(ctx_out, o.key, o.val_bool);
+ } else if (o.tag == LLAMA_KV_OVERRIDE_TYPE_STR) {
+ gguf_set_val_str(ctx_out, o.key, o.val_str);
+ } else {
+ LLAMA_LOG_WARN("%s: unknown KV override type for key %s\n", __func__, o.key);
+ }
+ }
+ }
+
+ for (int i = 0; i < ml.n_tensors; ++i) {
+ const struct ggml_tensor * meta = ml.get_tensor_meta(i);
+
+ const std::string name = ggml_get_name(meta);
+
+ // TODO: avoid hardcoded tensor names - use the TN_* constants
+ if (name.find("attn_v.weight") != std::string::npos ||
+ name.find("attn_qkv.weight") != std::string::npos) {
+ ++qs.n_attention_wv;
+ } else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) {
+ qs.has_output = true;
+ }
+ }
+
+ qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer;
+
+ // sanity checks
+ //
+ // - qs.n_attention_wv == 0 for Mamba models
+ // - qs.n_attention_wv == model.hparams.n_layer for Transformer models
+ // - qs.n_attention_wv == 3 * model.hparams.n_layer for Encoder-Decoder models
+ //
+ GGML_ASSERT((qs.n_attention_wv == 0 || qs.n_attention_wv == (int)model.hparams.n_layer || qs.n_attention_wv == 3 * (int)model.hparams.n_layer) && "n_attention_wv is unexpected");
+
+ size_t total_size_org = 0;
+ size_t total_size_new = 0;
+
+ std::vector<std::thread> workers;
+ workers.reserve(nthread);
+
+ int idx = 0;
+
+ std::vector<no_init<uint8_t>> read_data;
+ std::vector<no_init<uint8_t>> work;
+ std::vector<no_init<float>> f32_conv_buf;
+
+ uint16_t n_split = 1;
+ // Assume split index is continuous
+ if (params->keep_split) {
+ for (int i = 0; i < ml.n_tensors; ++i) {
+ n_split = std::max(uint16_t(ml.get_weight(i)->idx+1), n_split);
+ }
+ }
+ std::vector<gguf_context*> ctx_outs(n_split, NULL);
+ ctx_outs[0] = ctx_out;
+
+ // populate the original tensors so we get an initial meta data
+ for (int i = 0; i < ml.n_tensors; ++i) {
+ auto weight = ml.get_weight(i);
+ uint16_t i_split = params->keep_split ? weight->idx : 0;
+ struct ggml_tensor * tensor = weight->tensor;
+ if (ctx_outs[i_split] == NULL) {
+ ctx_outs[i_split] = gguf_init_empty();
+ }
+ gguf_add_tensor(ctx_outs[i_split], tensor);
+ }
+
+ // Set split info if needed
+ if (n_split > 1) {
+ for (size_t i = 0; i < ctx_outs.size(); ++i) {
+ gguf_set_val_u16(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_NO).c_str(), i);
+ gguf_set_val_u16(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_COUNT).c_str(), n_split);
+ gguf_set_val_i32(ctx_outs[i], ml.llm_kv(LLM_KV_SPLIT_TENSORS_COUNT).c_str(), ml.n_tensors);
+ }
+ }
+
+ int cur_split = -1;
+ std::ofstream fout;
+ auto close_ofstream = [&]() {
+ // Write metadata and close file handler
+ if (fout.is_open()) {
+ fout.seekp(0);
+ std::vector<uint8_t> data(gguf_get_meta_size(ctx_outs[cur_split]));
+ gguf_get_meta_data(ctx_outs[cur_split], data.data());
+ fout.write((const char *) data.data(), data.size());
+ fout.close();
+ }
+ };
+ auto new_ofstream = [&](int index) {
+ cur_split = index;
+ GGML_ASSERT(ctx_outs[cur_split] && "Find uninitialized gguf_context");
+ std::string fname = fname_out;
+ if (params->keep_split) {
+ char split_path[PATH_MAX] = {0};
+ llama_split_path(split_path, sizeof(split_path), fname_out.c_str(), cur_split, n_split);
+ fname = std::string(split_path);
+ }
+
+ fout = std::ofstream(fname, std::ios::binary);
+ fout.exceptions(std::ofstream::failbit); // fail fast on write errors
+ const size_t meta_size = gguf_get_meta_size(ctx_outs[cur_split]);
+ // placeholder for the meta data
+ ::zeros(fout, meta_size);
+ };
+
+ const auto tn = LLM_TN(model.arch);
+ new_ofstream(0);
+ for (int i = 0; i < ml.n_tensors; ++i) {
+ auto weight = ml.get_weight(i);
+ struct ggml_tensor * tensor = weight->tensor;
+ if (weight->idx != cur_split && params->keep_split) {
+ close_ofstream();
+ new_ofstream(weight->idx);
+ }
+
+ const std::string name = ggml_get_name(tensor);
+
+ if (!ml.use_mmap) {
+ if (read_data.size() < ggml_nbytes(tensor)) {
+ read_data.resize(ggml_nbytes(tensor));
+ }
+ tensor->data = read_data.data();
+ }
+ ml.load_data_for(tensor);
+
+ LLAMA_LOG_INFO("[%4d/%4d] %36s - [%s], type = %6s, ",
+ ++idx, ml.n_tensors,
+ ggml_get_name(tensor),
+ llama_format_tensor_shape(tensor).c_str(),
+ ggml_type_name(tensor->type));
+
+ // This used to be a regex, but <regex> has an extreme cost to compile times.
+ bool quantize = name.rfind("weight") == name.size() - 6; // ends with 'weight'?
+
+ // quantize only 2D and 3D tensors (experts)
+ quantize &= (ggml_n_dims(tensor) >= 2);
+
+ // do not quantize norm tensors
+ quantize &= name.find("_norm.weight") == std::string::npos;
+
+ quantize &= params->quantize_output_tensor || name != "output.weight";
+ quantize &= !params->only_copy;
+
+ // do not quantize expert gating tensors
+ // NOTE: can't use LLM_TN here because the layer number is not known
+ quantize &= name.find("ffn_gate_inp.weight") == std::string::npos;
+
+ // do not quantize positional embeddings and token types (BERT)
+ quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_POS_EMBD, "weight");
+ quantize &= name != LLM_TN(model.arch)(LLM_TENSOR_TOKEN_TYPES, "weight");
+
+ // do not quantize Mamba's small yet 2D weights
+ // NOTE: can't use LLM_TN here because the layer number is not known
+ quantize &= name.find("ssm_conv1d.weight") == std::string::npos;
+ quantize &= name.find("ssm_x.weight") == std::string::npos;
+ quantize &= name.find("ssm_dt.weight") == std::string::npos;
+
+ // do not quantize relative position bias (T5)
+ quantize &= name.find("attn_rel_b.weight") == std::string::npos;
+
+ enum ggml_type new_type;
+ void * new_data;
+ size_t new_size;
+
+ if (quantize) {
+ new_type = default_type;
+
+ // get more optimal quantization type based on the tensor shape, layer, etc.
+ if (!params->pure && ggml_is_quantized(default_type)) {
+ new_type = llama_tensor_get_type(qs, new_type, tensor, ftype);
+ }
+ if (params->token_embedding_type < GGML_TYPE_COUNT && strcmp(tensor->name, "token_embd.weight") == 0) {
+ new_type = params->token_embedding_type;
+ }
+ if (params->output_tensor_type < GGML_TYPE_COUNT && strcmp(tensor->name, "output.weight") == 0) {
+ new_type = params->output_tensor_type;
+ }
+
+ // If we've decided to quantize to the same type the tensor is already
+ // in then there's nothing to do.
+ quantize = tensor->type != new_type;
+ }
+
+ if (!quantize) {
+ new_type = tensor->type;
+ new_data = tensor->data;
+ new_size = ggml_nbytes(tensor);
+ LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1024.0/1024.0);
+ } else {
+ const int64_t nelements = ggml_nelements(tensor);
+
+ const float * imatrix = nullptr;
+ if (imatrix_data) {
+ auto it = imatrix_data->find(tensor->name);
+ if (it == imatrix_data->end()) {
+ LLAMA_LOG_INFO("\n====== %s: did not find weights for %s\n", __func__, tensor->name);
+ } else {
+ if (it->second.size() == (size_t)tensor->ne[0]*tensor->ne[2]) {
+ imatrix = it->second.data();
+ } else {
+ LLAMA_LOG_INFO("\n====== %s: imatrix size %d is different from tensor size %d for %s\n", __func__,
+ int(it->second.size()), int(tensor->ne[0]*tensor->ne[2]), tensor->name);
+
+ // this can happen when quantizing an old mixtral model with split tensors with a new incompatible imatrix
+ // this is a significant error and it may be good idea to abort the process if this happens,
+ // since many people will miss the error and not realize that most of the model is being quantized without an imatrix
+ // tok_embd should be ignored in this case, since it always causes this warning
+ if (name != tn(LLM_TENSOR_TOKEN_EMBD, "weight")) {
+ throw std::runtime_error(format("imatrix size %d is different from tensor size %d for %s",
+ int(it->second.size()), int(tensor->ne[0]*tensor->ne[2]), tensor->name));
+ }
+ }
+ }
+ }
+ if ((new_type == GGML_TYPE_IQ2_XXS ||
+ new_type == GGML_TYPE_IQ2_XS ||
+ new_type == GGML_TYPE_IQ2_S ||
+ new_type == GGML_TYPE_IQ1_S ||
+ (new_type == GGML_TYPE_IQ1_M && strcmp(tensor->name, "token_embd.weight") && strcmp(tensor->name, "output.weight")) ||
+ (new_type == GGML_TYPE_Q2_K && params->ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && strcmp(tensor->name, "token_embd.weight") != 0)) && !imatrix) {
+ LLAMA_LOG_ERROR("\n\n============================================================\n");
+ LLAMA_LOG_ERROR("Missing importance matrix for tensor %s in a very low-bit quantization\n", tensor->name);
+ LLAMA_LOG_ERROR("The result will be garbage, so bailing out\n");
+ LLAMA_LOG_ERROR("============================================================\n\n");
+ throw std::runtime_error(format("Missing importance matrix for tensor %s in a very low-bit quantization", tensor->name));
+ }
+
+ float * f32_data;
+
+ if (tensor->type == GGML_TYPE_F32) {
+ f32_data = (float *) tensor->data;
+ } else if (ggml_is_quantized(tensor->type) && !params->allow_requantize) {
+ throw std::runtime_error(format("requantizing from type %s is disabled", ggml_type_name(tensor->type)));
+ } else {
+ llama_tensor_dequantize_internal(tensor, f32_conv_buf, workers, nelements, nthread);
+ f32_data = (float *) f32_conv_buf.data();
+ }
+
+ int chunk_size_multiplier = 1;
+ if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8 || new_type == GGML_TYPE_Q4_0_8_8) {
+ if ((new_type == GGML_TYPE_Q4_0_8_8) && (tensor->ne[1] % 8 != 0)) new_type = GGML_TYPE_Q4_0;
+ else if (tensor->ne[1] % 4 != 0) new_type = GGML_TYPE_Q4_0;
+ if (new_type == GGML_TYPE_Q4_0_8_8) chunk_size_multiplier = 8;
+ else if (new_type == GGML_TYPE_Q4_0_4_4 || new_type == GGML_TYPE_Q4_0_4_8) chunk_size_multiplier = 4;
+ }
+
+ LLAMA_LOG_INFO("converting to %s .. ", ggml_type_name(new_type));
+ fflush(stdout);
+
+ if (work.size() < (size_t)nelements * 4) {
+ work.resize(nelements * 4); // upper bound on size
+ }
+ new_data = work.data();
+
+ const int64_t n_per_row = tensor->ne[0];
+ const int64_t nrows = tensor->ne[1];
+
+ static const int64_t min_chunk_size = 32 * 512;
+ const int64_t chunk_size = (n_per_row >= min_chunk_size ? n_per_row : n_per_row * ((min_chunk_size + n_per_row - 1)/n_per_row)) *
+ chunk_size_multiplier;
+
+ const int64_t nelements_matrix = tensor->ne[0] * tensor->ne[1];
+ const int64_t nchunk = (nelements_matrix + chunk_size - 1)/chunk_size;
+ const int64_t nthread_use = nthread > 1 ? std::max((int64_t)1, std::min((int64_t)nthread, nchunk)) : 1;
+
+ // quantize each expert separately since they have different importance matrices
+ new_size = 0;
+ for (int64_t i03 = 0; i03 < tensor->ne[2]; ++i03) {
+ const float * f32_data_03 = f32_data + i03 * nelements_matrix;
+ void * new_data_03 = (char *)new_data + ggml_row_size(new_type, n_per_row) * i03 * nrows;
+ const float * imatrix_03 = imatrix ? imatrix + i03 * n_per_row : nullptr;
+
+ new_size += llama_tensor_quantize_internal(new_type, f32_data_03, new_data_03, chunk_size, nrows, n_per_row, imatrix_03, workers, nthread_use);
+ }
+ LLAMA_LOG_INFO("size = %8.2f MiB -> %8.2f MiB\n", ggml_nbytes(tensor)/1024.0/1024.0, new_size/1024.0/1024.0);
+ }
+ total_size_org += ggml_nbytes(tensor);
+ total_size_new += new_size;
+
+ // update the gguf meta data as we go
+ gguf_set_tensor_type(ctx_outs[cur_split], name.c_str(), new_type);
+ gguf_set_tensor_data(ctx_outs[cur_split], name.c_str(), new_data, new_size);
+
+ // write tensor data + padding
+ fout.write((const char *) new_data, new_size);
+ zeros(fout, GGML_PAD(new_size, align) - new_size);
+ }
+ close_ofstream();
+ for (auto & c:ctx_outs) {
+ gguf_free(c);
+ }
+
+ LLAMA_LOG_INFO("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
+ LLAMA_LOG_INFO("%s: quant size = %8.2f MB\n", __func__, total_size_new/1024.0/1024.0);
+
+ if (qs.n_fallback > 0) {
+ LLAMA_LOG_WARN("%s: WARNING: %d of %d tensor(s) required fallback quantization\n",
+ __func__, qs.n_fallback, qs.n_k_quantized + qs.n_fallback);
+ }
+}
+
+static void llama_lora_adapter_init_internal(struct llama_model * model, const char * path_lora, struct llama_lora_adapter & adapter) {
+ LLAMA_LOG_INFO("%s: loading lora adapter from '%s' ...\n", __func__, path_lora);
+
+ ggml_context * ctx = nullptr;
+ struct gguf_init_params meta_gguf_params = {
+ /* .no_alloc = */ true,
+ /* .ctx = */ &ctx,
+ };
+ struct gguf_context * ctx_gguf = gguf_init_from_file(path_lora, meta_gguf_params);
+ if (!ctx_gguf) {
+ throw std::runtime_error("failed to load lora adapter file from " + std::string(path_lora));
+ }
+
+ // check metadata
+ {
+ auto get_kv_str = [&](const std::string & key) -> std::string {
+ int id = gguf_find_key(ctx_gguf, key.c_str());
+ return id < 0 ? "" : std::string(gguf_get_val_str(ctx_gguf, id));
+ };
+ auto get_kv_f32 = [&](const std::string & key) -> float {
+ int id = gguf_find_key(ctx_gguf, key.c_str());
+ return id < 0 ? 0.0f : gguf_get_val_f32(ctx_gguf, id);
+ };
+ LLM_KV llm_kv = LLM_KV(LLM_ARCH_UNKNOWN);
+
+ auto general_type = get_kv_str(llm_kv(LLM_KV_GENERAL_TYPE));
+ if (general_type != "adapter") {
+ gguf_free(ctx_gguf);
+ throw std::runtime_error("expect general.type to be 'adapter', but got: " + general_type);
+ }
+
+ auto general_arch_str = get_kv_str(llm_kv(LLM_KV_GENERAL_ARCHITECTURE));
+ auto general_arch = llm_arch_from_string(general_arch_str);
+ if (general_arch != model->arch) {
+ gguf_free(ctx_gguf);
+ throw std::runtime_error("model arch and LoRA arch mismatch");
+ }
+
+ auto adapter_type = get_kv_str(llm_kv(LLM_KV_ADAPTER_TYPE));
+ if (adapter_type != "lora") {
+ gguf_free(ctx_gguf);
+ throw std::runtime_error("expect adapter.type to be 'lora', but got: " + adapter_type);
+ }
+
+ adapter.alpha = get_kv_f32(llm_kv(LLM_KV_ADAPTER_LORA_ALPHA));
+ }
+
+ int n_tensors = gguf_get_n_tensors(ctx_gguf);
+
+ // contexts for each buffer type
+ std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
+ auto get_ctx_for_buft = [&](ggml_backend_buffer_type_t buft) -> ggml_context * {
+ auto it = ctx_map.find(buft);
+ if (it == ctx_map.end()) {
+ // add a new context
+ struct ggml_init_params params = {
+ /*.mem_size =*/ n_tensors*ggml_tensor_overhead(),
+ /*.mem_buffer =*/ NULL,
+ /*.no_alloc =*/ true,
+ };
+ ggml_context * buft_ctx = ggml_init(params);
+ ctx_map[buft] = buft_ctx;
+ return buft_ctx;
+ };
+ return it->second;
+ };
+
+ // bundle lora_a and lora_b into pairs
+ std::map<std::string, llama_lora_weight> ab_map;
+ auto str_endswith = [](const std::string & str, const std::string & suffix) {
+ return str.size() >= suffix.size() && str.compare(str.size()-suffix.size(), suffix.size(), suffix) == 0;
+ };
+ for (ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
+ std::string name(cur->name);
+ if (str_endswith(name, ".lora_a")) {
+ replace_all(name, ".lora_a", "");
+ if (ab_map.find(name) == ab_map.end()) {
+ ab_map[name] = llama_lora_weight(cur, nullptr);
+ } else {
+ ab_map[name].a = cur;
+ }
+ } else if (str_endswith(name, ".lora_b")) {
+ replace_all(name, ".lora_b", "");
+ if (ab_map.find(name) == ab_map.end()) {
+ ab_map[name] = llama_lora_weight(nullptr, cur);
+ } else {
+ ab_map[name].b = cur;
+ }
+ } else {
+ gguf_free(ctx_gguf);
+ ggml_free(ctx);
+ throw std::runtime_error("LoRA tensor '" + name + "' has unexpected suffix");
+ }
+ }
+
+ // add tensors
+ for (auto & it : ab_map) {
+ const std::string & name = it.first;
+ llama_lora_weight & w = it.second;
+
+ if (!w.a || !w.b) {
+ gguf_free(ctx_gguf);
+ ggml_free(ctx);
+ throw std::runtime_error("LoRA tensor pair for '" + name + "' is missing one component");
+ }
+
+ // device buft and device ctx
+ auto * model_tensor = llama_get_model_tensor(model, name.c_str());
+ if (!model_tensor) {
+ gguf_free(ctx_gguf);
+ ggml_free(ctx);
+ throw std::runtime_error("LoRA tensor '" + name + "' does not exist in base model");
+ }
+ struct ggml_context * dev_ctx = get_ctx_for_buft(ggml_backend_buffer_get_type(model_tensor->buffer));
+ // validate tensor shape
+ if (model_tensor->ne[0] != w.a->ne[0] || model_tensor->ne[1] != w.b->ne[1]) {
+ gguf_free(ctx_gguf);
+ ggml_free(ctx);
+ throw std::runtime_error("tensor '" + name + "' has incorrect shape");
+ }
+ if (w.a->ne[1] != w.b->ne[0]) {
+ gguf_free(ctx_gguf);
+ ggml_free(ctx);
+ throw std::runtime_error("lora_a tensor is not transposed (hint: adapter from \"finetune\" example is no longer supported)");
+ }
+ // save tensor to adapter
+ struct ggml_tensor * tensor_a = ggml_dup_tensor(dev_ctx, w.a);
+ struct ggml_tensor * tensor_b = ggml_dup_tensor(dev_ctx, w.b);
+ ggml_set_name(tensor_a, w.a->name);
+ ggml_set_name(tensor_b, w.b->name);
+ adapter.ab_map[name] = llama_lora_weight(tensor_a, tensor_b);
+ }
+
+ // allocate tensors / buffers and zero
+ {
+ adapter.ctxs.reserve(ctx_map.size());
+ adapter.bufs.reserve(ctx_map.size());
+ for (auto it : ctx_map) {
+ ggml_backend_buffer_type_t buft = it.first;
+ ggml_context * ctx_dev = it.second;
+ ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx_dev, buft);
+ if (!buf) {
+ gguf_free(ctx_gguf);
+ ggml_free(ctx);
+ throw std::runtime_error("failed to allocate buffer for lora adapter\n");
+ }
+ LLAMA_LOG_INFO("%s: %10s LoRA buffer size = %8.2f MiB\n", __func__, ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf)/1024.0/1024.0);
+ adapter.ctxs.push_back(ctx_dev);
+ adapter.bufs.push_back(buf);
+ }
+ }
+
+ // set tensor data
+ {
+ llama_file gguf_file(path_lora, "rb");
+ std::vector<uint8_t> read_buf;
+ auto set_tensor = [&](struct ggml_tensor * orig, struct ggml_tensor * dev) {
+ size_t offs = gguf_get_data_offset(ctx_gguf) + gguf_get_tensor_offset(ctx_gguf, gguf_find_tensor(ctx_gguf, orig->name));
+ size_t size = ggml_nbytes(orig);
+ read_buf.resize(size);
+ gguf_file.seek(offs, SEEK_SET);
+ gguf_file.read_raw(read_buf.data(), size);
+ ggml_backend_tensor_set(dev, read_buf.data(), 0, size);
+ };
+ for (auto & it : adapter.ab_map) {
+ auto orig = ab_map[it.first];
+ auto dev = it.second;
+ set_tensor(orig.a, dev.a);
+ set_tensor(orig.b, dev.b);
+ }
+ }
+
+ LLAMA_LOG_INFO("%s: loaded %ld tensors from lora file\n", __func__, adapter.ab_map.size()*2);
+
+ // free ctx for reading gguf
+ gguf_free(ctx_gguf);
+ ggml_free(ctx);
+}
+
+int32_t llama_lora_adapter_set(
+ struct llama_context * ctx,
+ struct llama_lora_adapter * adapter,
+ float scale) {
+ if (ctx->cparams.flash_attn) {
+ LLAMA_LOG_ERROR("%s: flash_attn is not compatible with LoRA\n", __func__);
+ return -1;
+ }
+ ctx->lora_adapters[adapter] = scale;
+ return 0;
+}
+
+int32_t llama_lora_adapter_remove(
+ struct llama_context * ctx,
+ struct llama_lora_adapter * adapter) {
+ auto pos = ctx->lora_adapters.find(adapter);
+ if (pos != ctx->lora_adapters.end()) {
+ ctx->lora_adapters.erase(pos);
+ return 0;
+ }
+ return -1;
+}
+
+void llama_lora_adapter_clear(struct llama_context * ctx) {
+ ctx->lora_adapters.clear();
+}
+
+void llama_lora_adapter_free(struct llama_lora_adapter * adapter) {
+ delete adapter;
+}
+
+//
+// interface implementation
+//
+struct llama_model_params llama_model_default_params() {
+ struct llama_model_params result = {
+ /*.n_gpu_layers =*/ 0,
+ /*.split_mode =*/ LLAMA_SPLIT_MODE_LAYER,
+ /*.main_gpu =*/ 0,
+ /*.tensor_split =*/ nullptr,
+ /*.rpc_servers =*/ nullptr,
+ /*.progress_callback =*/ nullptr,
+ /*.progress_callback_user_data =*/ nullptr,
+ /*.kv_overrides =*/ nullptr,
+ /*.vocab_only =*/ false,
+ /*.use_mmap =*/ true,
+ /*.use_mlock =*/ false,
+ /*.check_tensors =*/ false,
+ };
+
+#ifdef GGML_USE_METAL
+ // note: we usually have plenty of VRAM, so by default offload all layers to the GPU
+ result.n_gpu_layers = 999;
+#endif
+
+ return result;
+}
+
+struct llama_context_params llama_context_default_params() {
+ struct llama_context_params result = {
+ /*.seed =*/ LLAMA_DEFAULT_SEED,
+ /*.n_ctx =*/ 512,
+ /*.n_batch =*/ 2048,
+ /*.n_ubatch =*/ 512,
+ /*.n_seq_max =*/ 1,
+ /*.n_threads =*/ GGML_DEFAULT_N_THREADS, // TODO: better default
+ /*.n_threads_batch =*/ GGML_DEFAULT_N_THREADS,
+ /*.rope_scaling_type =*/ LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED,
+ /*.pooling_type =*/ LLAMA_POOLING_TYPE_UNSPECIFIED,
+ /*.attention_type =*/ LLAMA_ATTENTION_TYPE_UNSPECIFIED,
+ /*.rope_freq_base =*/ 0.0f,
+ /*.rope_freq_scale =*/ 0.0f,
+ /*.yarn_ext_factor =*/ -1.0f,
+ /*.yarn_attn_factor =*/ 1.0f,
+ /*.yarn_beta_fast =*/ 32.0f,
+ /*.yarn_beta_slow =*/ 1.0f,
+ /*.yarn_orig_ctx =*/ 0,
+ /*.defrag_thold =*/ -1.0f,
+ /*.cb_eval =*/ nullptr,
+ /*.cb_eval_user_data =*/ nullptr,
+ /*.type_k =*/ GGML_TYPE_F16,
+ /*.type_v =*/ GGML_TYPE_F16,
+ /*.logits_all =*/ false,
+ /*.embeddings =*/ false,
+ /*.offload_kqv =*/ true,
+ /*.flash_attn =*/ false,
+ /*.abort_callback =*/ nullptr,
+ /*.abort_callback_data =*/ nullptr,
+ };
+
+ return result;
+}
+
+struct llama_model_quantize_params llama_model_quantize_default_params() {
+ struct llama_model_quantize_params result = {
+ /*.nthread =*/ 0,
+ /*.ftype =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
+ /*.output_tensor_type =*/ GGML_TYPE_COUNT,
+ /*.token_embedding_type =*/ GGML_TYPE_COUNT,
+ /*.allow_requantize =*/ false,
+ /*.quantize_output_tensor =*/ true,
+ /*.only_copy =*/ false,
+ /*.pure =*/ false,
+ /*.keep_split =*/ false,
+ /*.imatrix =*/ nullptr,
+ /*.kv_overrides =*/ nullptr,
+ };
+
+ return result;
+}
+
+size_t llama_max_devices(void) {
+#if defined(GGML_USE_RPC)
+ return GGML_RPC_MAX_SERVERS;
+#elif defined(GGML_USE_METAL)
+ return 1;
+#elif defined(GGML_USE_CUDA)
+ return GGML_CUDA_MAX_DEVICES;
+#elif defined(GGML_USE_SYCL)
+ return GGML_SYCL_MAX_DEVICES;
+#elif defined(GGML_USE_VULKAN)
+ return GGML_VK_MAX_DEVICES;
+#elif defined(GGML_USE_CANN)
+ return GGML_CANN_MAX_DEVICES;
+#else
+ return 1;
+#endif
+}
+
+bool llama_supports_mmap(void) {
+ return llama_mmap::SUPPORTED;
+}
+
+bool llama_supports_mlock(void) {
+ return llama_mlock::SUPPORTED;
+}
+
+bool llama_supports_gpu_offload(void) {
+#if defined(GGML_USE_CUDA) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \
+ defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) || defined(GGML_USE_RPC)
+ // Defined when llama.cpp is compiled with support for offloading model layers to GPU.
+ return true;
+#else
+ return false;
+#endif
+}
+
+void llama_backend_init(void) {
+ ggml_time_init();
+
+ // needed to initialize f16 tables
+ {
+ struct ggml_init_params params = { 0, NULL, false };
+ struct ggml_context * ctx = ggml_init(params);
+ ggml_free(ctx);
+ }
+}
+
+void llama_numa_init(enum ggml_numa_strategy numa) {
+ if (numa != GGML_NUMA_STRATEGY_DISABLED) {
+ ggml_numa_init(numa);
+ }
+}
+
+void llama_backend_free(void) {
+ ggml_quantize_free();
+}
+
+int64_t llama_time_us(void) {
+ return ggml_time_us();
+}
+
+struct llama_model * llama_load_model_from_file(
+ const char * path_model,
+ struct llama_model_params params) {
+ ggml_time_init();
+
+ llama_model * model = new llama_model;
+
+ unsigned cur_percentage = 0;
+ if (params.progress_callback == NULL) {
+ params.progress_callback_user_data = &cur_percentage;
+ params.progress_callback = [](float progress, void * ctx) {
+ unsigned * cur_percentage_p = (unsigned *) ctx;
+ unsigned percentage = (unsigned) (100 * progress);
+ while (percentage > *cur_percentage_p) {
+ *cur_percentage_p = percentage;
+ LLAMA_LOG_INFO(".");
+ if (percentage >= 100) {
+ LLAMA_LOG_INFO("\n");
+ }
+ }
+ return true;
+ };
+ }
+ if (params.rpc_servers != nullptr && params.rpc_servers[0] != '\0') {
+ // split the servers set them into model->rpc_servers
+ std::string servers(params.rpc_servers);
+ size_t pos = 0;
+ while ((pos = servers.find(",")) != std::string::npos) {
+ std::string server = servers.substr(0, pos);
+ model->rpc_servers.push_back(server);
+ servers.erase(0, pos + 1);
+ }
+ model->rpc_servers.push_back(servers);
+ }
+ int status = llama_model_load(path_model, *model, params);
+ GGML_ASSERT(status <= 0);
+ if (status < 0) {
+ if (status == -1) {
+ LLAMA_LOG_ERROR("%s: failed to load model\n", __func__);
+ } else if (status == -2) {
+ LLAMA_LOG_INFO("%s: cancelled model load\n", __func__);
+ }
+ delete model;
+ return nullptr;
+ }
+
+ return model;
+}
+
+void llama_free_model(struct llama_model * model) {
+ delete model;
+}
+
+struct llama_context * llama_new_context_with_model(
+ struct llama_model * model,
+ struct llama_context_params params) {
+
+ if (!model) {
+ LLAMA_LOG_ERROR("%s: model cannot be NULL\n", __func__);
+ return nullptr;
+ }
+
+ if (params.n_batch == 0 && params.n_ubatch == 0) {
+ LLAMA_LOG_ERROR("%s: n_batch and n_ubatch cannot both be zero\n", __func__);
+ return nullptr;
+ }
+
+ if (params.n_ctx == 0 && model->hparams.n_ctx_train == 0) {
+ LLAMA_LOG_ERROR("%s: n_ctx and model->hparams.n_ctx_train cannot both be zero\n", __func__);
+ return nullptr;
+ }
+
+ if (params.flash_attn && model->arch == LLM_ARCH_GROK) {
+ LLAMA_LOG_WARN("%s: flash_attn is not compatible with Grok - forcing off\n", __func__);
+ params.flash_attn = false;
+ }
+
+ if (params.flash_attn && model->hparams.attn_soft_cap) {
+ LLAMA_LOG_WARN("%s: flash_attn is not compatible with attn_soft_cap - forcing off\n", __func__);
+ params.flash_attn = false;
+ }
+
+
+ if (params.flash_attn && model->hparams.n_embd_head_k != model->hparams.n_embd_head_v) {
+ LLAMA_LOG_WARN("%s: flash_attn requires n_embd_head_k == n_embd_head_v - forcing off\n", __func__);
+ params.flash_attn = false;
+ }
+
+ if (params.type_v != GGML_TYPE_F16 && !params.flash_attn) {
+ LLAMA_LOG_ERROR("%s: V cache quantization requires flash_attn\n", __func__);
+ return nullptr;
+ }
+
+ llama_context * ctx = new llama_context(*model);
+
+ const auto & hparams = model->hparams;
+ auto & cparams = ctx->cparams;
+
+ cparams.n_seq_max = std::max(1u, params.n_seq_max);
+ cparams.n_threads = params.n_threads;
+ cparams.n_threads_batch = params.n_threads_batch;
+ cparams.yarn_ext_factor = params.yarn_ext_factor;
+ cparams.yarn_attn_factor = params.yarn_attn_factor;
+ cparams.yarn_beta_fast = params.yarn_beta_fast;
+ cparams.yarn_beta_slow = params.yarn_beta_slow;
+ cparams.defrag_thold = params.defrag_thold;
+ cparams.embeddings = params.embeddings;
+ cparams.offload_kqv = params.offload_kqv;
+ cparams.flash_attn = params.flash_attn;
+ cparams.pooling_type = params.pooling_type;
+
+ cparams.n_ctx = params.n_ctx == 0 ? hparams.n_ctx_train : params.n_ctx;
+ cparams.rope_freq_base = params.rope_freq_base == 0.0f ? hparams.rope_freq_base_train : params.rope_freq_base;
+ cparams.rope_freq_scale = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale;
+
+ // this is necessary due to kv_self.n being padded later during inference
+ cparams.n_ctx = GGML_PAD(cparams.n_ctx, llama_kv_cache_get_padding(cparams));
+
+ // with causal attention, the batch size is limited by the context size
+ cparams.n_batch = hparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch;
+
+ // the batch has to be at least GGML_KQ_MASK_PAD because we will be padding the KQ_mask
+ // this is required by GPU kernels in order to avoid out-of-bounds accesses (e.g. ggml_flash_attn_ext)
+ // ref: https://github.com/ggerganov/llama.cpp/pull/5021
+ if (cparams.n_batch < GGML_KQ_MASK_PAD) {
+ LLAMA_LOG_WARN("%s: n_batch is less than GGML_KQ_MASK_PAD - increasing to %d\n", __func__, GGML_KQ_MASK_PAD);
+ cparams.n_batch = GGML_KQ_MASK_PAD;
+ }
+
+ cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
+
+ cparams.n_ctx_orig_yarn = params.yarn_orig_ctx != 0 ? params.yarn_orig_ctx :
+ hparams.n_ctx_orig_yarn != 0 ? hparams.n_ctx_orig_yarn :
+ hparams.n_ctx_train;
+
+ cparams.cb_eval = params.cb_eval;
+ cparams.cb_eval_user_data = params.cb_eval_user_data;
+
+ auto rope_scaling_type = params.rope_scaling_type;
+ if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED) {
+ rope_scaling_type = hparams.rope_scaling_type_train;
+ }
+
+ if (rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_NONE) {
+ cparams.rope_freq_scale = 1.0f; // never scale if scaling type is none
+ }
+
+ if (cparams.yarn_ext_factor < 0.0f) { // negative indicates 'not set'
+ cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f;
+ }
+
+ cparams.yarn_attn_factor *= hparams.rope_attn_factor;
+
+ if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
+ if (hparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
+ cparams.pooling_type = LLAMA_POOLING_TYPE_NONE;
+ } else {
+ cparams.pooling_type = hparams.pooling_type;
+ }
+ }
+
+ if (params.attention_type == LLAMA_ATTENTION_TYPE_UNSPECIFIED) {
+ cparams.causal_attn = hparams.causal_attn;
+ } else {
+ cparams.causal_attn = params.attention_type == LLAMA_ATTENTION_TYPE_CAUSAL;
+ }
+
+ if (params.seed == LLAMA_DEFAULT_SEED) {
+ params.seed = time(NULL);
+ }
+
+ LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, cparams.n_ctx);
+ LLAMA_LOG_INFO("%s: n_batch = %u\n", __func__, cparams.n_batch);
+ LLAMA_LOG_INFO("%s: n_ubatch = %u\n", __func__, cparams.n_ubatch);
+ LLAMA_LOG_INFO("%s: flash_attn = %d\n", __func__, cparams.flash_attn);
+ LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base);
+ LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale);
+
+ ctx->abort_callback = params.abort_callback;
+ ctx->abort_callback_data = params.abort_callback_data;
+
+ ctx->sampling.rng = std::mt19937(params.seed);
+ ctx->logits_all = params.logits_all;
+
+ uint32_t kv_size = cparams.n_ctx;
+ ggml_type type_k = params.type_k;
+ ggml_type type_v = params.type_v;
+
+ // Mamba only needs a constant number of KV cache cells per sequence
+ if (model->arch == LLM_ARCH_MAMBA) {
+ // Mamba needs at least as many KV cells as there are sequences kept at any time
+ kv_size = std::max((uint32_t) 1, params.n_seq_max);
+ // it's probably best to keep as much precision as possible for the states
+ type_k = GGML_TYPE_F32; // required by ggml_ssm_conv for Mamba's conv_states
+ type_v = GGML_TYPE_F32; // required by ggml_ssm_scan for Mamba's ssm_states
+ }
+
+ GGML_ASSERT(hparams.n_embd_head_k % ggml_blck_size(type_k) == 0);
+ GGML_ASSERT(hparams.n_embd_head_v % ggml_blck_size(type_v) == 0);
+
+ if (!hparams.vocab_only) {
+ // initialize backends
+#if defined(GGML_USE_METAL)
+ if (model->n_gpu_layers > 0) {
+ ctx->backend_metal = ggml_backend_metal_init();
+ if (ctx->backend_metal == nullptr) {
+ LLAMA_LOG_ERROR("%s: failed to initialize Metal backend\n", __func__);
+ llama_free(ctx);
+ return nullptr;
+ }
+ ctx->backends.push_back(ctx->backend_metal);
+ }
+#elif defined(GGML_USE_CUDA)
+ if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
+ // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
+ ggml_backend_t backend = ggml_backend_cuda_init(model->main_gpu);
+ if (backend == nullptr) {
+ LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, model->main_gpu);
+ llama_free(ctx);
+ return nullptr;
+ }
+ ctx->backends.push_back(backend);
+ } else {
+ // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
+ for (int device = 0; device < ggml_backend_cuda_get_device_count(); ++device) {
+ ggml_backend_t backend = ggml_backend_cuda_init(device);
+ if (backend == nullptr) {
+ LLAMA_LOG_ERROR("%s: failed to initialize CUDA%d backend\n", __func__, device);
+ llama_free(ctx);
+ return nullptr;
+ }
+ ctx->backends.push_back(backend);
+ }
+ }
+#elif defined(GGML_USE_VULKAN)
+ if (model->split_mode == LLAMA_SPLIT_MODE_ROW) {
+ LLAMA_LOG_ERROR("%s: Row split not supported. Failed to initialize Vulkan backend\n", __func__);
+ llama_free(ctx);
+ return nullptr;
+ }
+ if (model->split_mode == LLAMA_SPLIT_MODE_NONE) {
+ ggml_backend_t backend = ggml_backend_vk_init(model->main_gpu);
+ if (backend == nullptr) {
+ LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__);
+ llama_free(ctx);
+ return nullptr;
+ }
+ ctx->backends.push_back(backend);
+ } else {
+ for (int device = 0; device < ggml_backend_vk_get_device_count(); ++device) {
+ ggml_backend_t backend = ggml_backend_vk_init(device);
+ if (backend == nullptr) {
+ LLAMA_LOG_ERROR("%s: failed to initialize Vulkan%d backend\n", __func__, device);
+ llama_free(ctx);
+ return nullptr;
+ }
+ ctx->backends.push_back(backend);
+ }
+ }
+#elif defined(GGML_USE_SYCL)
+ // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
+ if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
+ 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);
+ } else {
+ // LLAMA_SPLIT_LAYER requires a backend for each GPU
+ for (int i = 0; i < ggml_backend_sycl_get_device_count(); ++i) {
+ ggml_backend_t backend = ggml_backend_sycl_init(i);
+ if (backend == nullptr) {
+ LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d for No.%d backend\n", __func__, i, i);
+ llama_free(ctx);
+ return nullptr;
+ }
+ ctx->backends.push_back(backend);
+ }
+ }
+#elif defined(GGML_USE_KOMPUTE)
+ if (model->n_gpu_layers > 0) {
+ auto * backend = ggml_backend_kompute_init(model->main_gpu);
+ if (backend == nullptr) {
+ LLAMA_LOG_ERROR("%s: failed to initialize Kompute backend\n", __func__);
+ llama_free(ctx);
+ return nullptr;
+ }
+ ctx->backends.push_back(backend);
+ }
+#elif defined(GGML_USE_CANN)
+ // with split_mode LLAMA_SPLIT_MODE_NONE or LLAMA_SPLIT_MODE_ROW, only the main GPU backend is used
+ // TODO: ggml_backend_cann is not support split tensor now, just leave code here.
+ if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
+ ggml_backend_t backend = ggml_backend_cann_init(model->main_gpu);
+ if (backend == nullptr) {
+ LLAMA_LOG_ERROR("%s: failed to initialize CANN%d backend\n", __func__, model->main_gpu);
+ llama_free(ctx);
+ return nullptr;
+ }
+ ctx->backends.push_back(backend);
+ } else {
+ // LLAMA_SPLIT_MODE_LAYER requires a backend for each GPU
+ // TODO: currently, CANN can't use multi-gpus, just leave code here for further cann version.
+ for (int32_t device = 0; device < ggml_backend_cann_get_device_count(); ++device) {
+ ggml_backend_t backend = ggml_backend_cann_init(device);
+ if (backend == nullptr) {
+ LLAMA_LOG_ERROR("%s: failed to initialize CANN%d backend\n", __func__, device);
+ llama_free(ctx);
+ return nullptr;
+ }
+ ctx->backends.push_back(backend);
+ }
+ }
+#endif
+
+#ifdef GGML_USE_BLAS
+ ctx->backend_blas = ggml_backend_blas_init();
+ if (ctx->backend_blas == nullptr) {
+ LLAMA_LOG_WARN("%s: failed to initialize BLAS backend\n", __func__);
+ } else {
+ ctx->backends.push_back(ctx->backend_blas);
+ }
+#endif
+
+#if defined(GGML_USE_RPC)
+ if (model->n_gpu_layers > 0) {
+ for (const auto & endpoint : model->rpc_servers) {
+ ggml_backend_t backend = ggml_backend_rpc_init(endpoint.c_str());
+ if (backend == nullptr) {
+ LLAMA_LOG_ERROR("%s: failed to initialize RPC to '%s'\n", __func__, endpoint.c_str());
+ llama_free(ctx);
+ return nullptr;
+ }
+ ctx->backends.push_back(backend);
+ }
+ }
+#endif
+ ctx->backend_cpu = ggml_backend_cpu_init();
+ if (ctx->backend_cpu == nullptr) {
+ LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__);
+ llama_free(ctx);
+ return nullptr;
+ }
+ ctx->backends.push_back(ctx->backend_cpu);
+
+ if (!llama_kv_cache_init(ctx->kv_self, ctx, type_k, type_v, kv_size, cparams.offload_kqv)) {
+ LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__);
+ llama_free(ctx);
+ return nullptr;
+ }
+
+ {
+ size_t memory_size_k = 0;
+ size_t memory_size_v = 0;
+
+ for (auto & k : ctx->kv_self.k_l) {
+ memory_size_k += ggml_nbytes(k);
+ }
+
+ for (auto & v : ctx->kv_self.v_l) {
+ memory_size_v += ggml_nbytes(v);
+ }
+
+ LLAMA_LOG_INFO("%s: KV self size = %7.2f MiB, K (%s): %7.2f MiB, V (%s): %7.2f MiB\n", __func__,
+ (float)(memory_size_k + memory_size_v) / (1024.0f * 1024.0f),
+ ggml_type_name(type_k), (float)memory_size_k / (1024.0f * 1024.0f),
+ ggml_type_name(type_v), (float)memory_size_v / (1024.0f * 1024.0f));
+ }
+
+ // graph outputs buffer
+ {
+ // resized during inference when a batch uses more outputs
+ if (llama_output_reserve(*ctx, params.n_seq_max) < params.n_seq_max) {
+ LLAMA_LOG_ERROR("%s: failed to reserve initial output buffer\n", __func__);
+ llama_free(ctx);
+ return nullptr;
+ }
+
+ LLAMA_LOG_INFO("%s: %10s output buffer size = %8.2f MiB\n", __func__,
+ ggml_backend_buffer_name(ctx->buf_output),
+ ggml_backend_buffer_get_size(ctx->buf_output) / 1024.0 / 1024.0);
+ }
+
+ // scheduler and compute buffers
+ {
+ // buffer types used for the compute buffer of each backend
+ std::vector<ggml_backend_buffer_type_t> backend_buft;
+ for (auto * backend : ctx->backends) {
+ if (ggml_backend_is_cpu(backend)) {
+ // use host buffers for the CPU backend compute buffer
+ backend_buft.push_back(llama_default_buffer_type_cpu(true));
+ } else {
+ backend_buft.push_back(ggml_backend_get_default_buffer_type(backend));
+ }
+ }
+
+ // buffer used to store the computation graph and the tensor meta data
+ ctx->buf_compute_meta.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead_custom(LLAMA_MAX_NODES, false));
+
+ // enabling pipeline parallelism in the scheduler increases memory usage, so it is only done when necessary
+ bool pipeline_parallel =
+ llama_get_device_count(*model) > 1 &&
+ model->n_gpu_layers > (int)model->hparams.n_layer &&
+ model->split_mode == LLAMA_SPLIT_MODE_LAYER &&
+ params.offload_kqv;
+#ifndef GGML_USE_CUDA
+ // pipeline parallelism requires support for async compute and events
+ // currently this is only implemented in the CUDA backend
+ pipeline_parallel = false;
+#endif
+ ctx->sched = ggml_backend_sched_new(ctx->backends.data(), backend_buft.data(), ctx->backends.size(), LLAMA_MAX_NODES, pipeline_parallel);
+
+ if (pipeline_parallel) {
+ LLAMA_LOG_INFO("%s: pipeline parallelism enabled (n_copies=%d)\n", __func__, ggml_backend_sched_get_n_copies(ctx->sched));
+ }
+
+ // build worst-case graph
+ int n_tokens = (int)std::min(cparams.n_ctx, cparams.n_ubatch);
+ int n_past = cparams.n_ctx - n_tokens;
+ llama_token token = llama_token_bos(&ctx->model); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
+ ggml_cgraph * gf = llama_build_graph(*ctx, llama_batch_get_one(&token, n_tokens, n_past, 0), true);
+
+ // initialize scheduler with the worst-case graph
+ if (!ggml_backend_sched_reserve(ctx->sched, gf)) {
+ LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
+ llama_free(ctx);
+ return nullptr;
+ }
+
+ for (size_t i = 0; i < ctx->backends.size(); i++) {
+ ggml_backend_t backend = ctx->backends[i];
+ ggml_backend_buffer_type_t buft = backend_buft[i];
+ size_t size = ggml_backend_sched_get_buffer_size(ctx->sched, backend);
+ if (size > 1) {
+ LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
+ ggml_backend_buft_name(buft),
+ size / 1024.0 / 1024.0);
+ }
+ }
+
+ // note: the number of splits during measure is higher than during inference due to the kv shift
+ int n_splits = ggml_backend_sched_get_n_splits(ctx->sched);
+ LLAMA_LOG_INFO("%s: graph nodes = %d\n", __func__, gf->n_nodes);
+ LLAMA_LOG_INFO("%s: graph splits = %d\n", __func__, n_splits);
+ }
+ }
+
+ return ctx;
+}
+
+void llama_free(struct llama_context * ctx) {
+ delete ctx;
+}
+
+const struct llama_model * llama_get_model(const struct llama_context * ctx) {
+ return &ctx->model;
+}
+
+const struct llama_vocab * llama_get_vocab(const struct llama_context * ctx) {
+ return &ctx->model.vocab;
+}
+
+uint32_t llama_n_ctx(const struct llama_context * ctx) {
+ return ctx->cparams.n_ctx;
+}
+
+uint32_t llama_n_batch(const struct llama_context * ctx) {
+ return ctx->cparams.n_batch;
+}
+
+uint32_t llama_n_ubatch(const struct llama_context * ctx) {
+ return ctx->cparams.n_ubatch;
+}
+
+uint32_t llama_n_seq_max(const struct llama_context * ctx) {
+ return ctx->kv_self.size;
+}
+
+enum llama_vocab_type llama_vocab_type(const struct llama_model * model) {
+ return model->vocab.type;
+}
+
+enum llama_rope_type llama_rope_type(const struct llama_model * model) {
+ switch (model->arch) {
+ // these models do not use RoPE
+ case LLM_ARCH_GPT2:
+ case LLM_ARCH_GPTJ:
+ case LLM_ARCH_MPT:
+ case LLM_ARCH_REFACT:
+ case LLM_ARCH_BLOOM:
+ case LLM_ARCH_MAMBA:
+ case LLM_ARCH_JINA_BERT_V2:
+ case LLM_ARCH_T5:
+ case LLM_ARCH_JAIS:
+ return LLAMA_ROPE_TYPE_NONE;
+
+ // use what we call a normal RoPE, operating on pairs of consecutive head values
+ case LLM_ARCH_LLAMA:
+ case LLM_ARCH_BAICHUAN:
+ case LLM_ARCH_STARCODER:
+ case LLM_ARCH_PLAMO:
+ case LLM_ARCH_ORION:
+ case LLM_ARCH_INTERNLM2:
+ case LLM_ARCH_MINICPM:
+ case LLM_ARCH_XVERSE:
+ case LLM_ARCH_COMMAND_R:
+ case LLM_ARCH_OLMO:
+ case LLM_ARCH_ARCTIC:
+ case LLM_ARCH_DEEPSEEK2:
+ case LLM_ARCH_CHATGLM:
+ return LLAMA_ROPE_TYPE_NORM;
+
+ // the pairs of head values are offset by n_rot/2
+ case LLM_ARCH_FALCON:
+ case LLM_ARCH_GROK:
+ case LLM_ARCH_DBRX:
+ case LLM_ARCH_BERT:
+ case LLM_ARCH_NOMIC_BERT:
+ case LLM_ARCH_STABLELM:
+ case LLM_ARCH_BITNET:
+ case LLM_ARCH_QWEN:
+ case LLM_ARCH_QWEN2:
+ case LLM_ARCH_QWEN2MOE:
+ case LLM_ARCH_PHI2:
+ case LLM_ARCH_PHI3:
+ case LLM_ARCH_GEMMA:
+ case LLM_ARCH_GEMMA2:
+ case LLM_ARCH_STARCODER2:
+ case LLM_ARCH_OPENELM:
+ case LLM_ARCH_GPTNEOX:
+ case LLM_ARCH_CODESHELL:
+ return LLAMA_ROPE_TYPE_NEOX;
+
+ // all model arches should be listed explicitly here
+ case LLM_ARCH_UNKNOWN:
+ GGML_ASSERT(false && "unknown architecture");
+ break;
+ }
+
+ return LLAMA_ROPE_TYPE_NONE;
+}
+
+enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx) {
+ return ctx->cparams.pooling_type;
+}
+
+int32_t llama_n_vocab(const struct llama_model * model) {
+ return model->hparams.n_vocab;
+}
+
+int32_t llama_n_ctx_train(const struct llama_model * model) {
+ return model->hparams.n_ctx_train;
+}
+
+int32_t llama_n_embd(const struct llama_model * model) {
+ return model->hparams.n_embd;
+}
+
+int32_t llama_n_layer(const struct llama_model * model) {
+ return model->hparams.n_layer;
+}
+
+float llama_rope_freq_scale_train(const struct llama_model * model) {
+ return model->hparams.rope_freq_scale_train;
+}
+
+int32_t llama_model_meta_val_str(const struct llama_model * model, const char * key, char * buf, size_t buf_size) {
+ const auto & it = model->gguf_kv.find(key);
+ if (it == model->gguf_kv.end()) {
+ if (buf_size > 0) {
+ buf[0] = '\0';
+ }
+ return -1;
+ }
+ return snprintf(buf, buf_size, "%s", it->second.c_str());
+}
+
+int32_t llama_model_meta_count(const struct llama_model * model) {
+ return (int)model->gguf_kv.size();
+}
+
+int32_t llama_model_meta_key_by_index(const struct llama_model * model, int i, char * buf, size_t buf_size) {
+ if (i < 0 || i >= (int)model->gguf_kv.size()) {
+ if (buf_size > 0) {
+ buf[0] = '\0';
+ }
+ return -1;
+ }
+ auto it = model->gguf_kv.begin();
+ std::advance(it, i);
+ return snprintf(buf, buf_size, "%s", it->first.c_str());
+}
+
+int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int32_t i, char * buf, size_t buf_size) {
+ if (i < 0 || i >= (int)model->gguf_kv.size()) {
+ if (buf_size > 0) {
+ buf[0] = '\0';
+ }
+ return -1;
+ }
+ auto it = model->gguf_kv.begin();
+ std::advance(it, i);
+ return snprintf(buf, buf_size, "%s", it->second.c_str());
+}
+
+int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) {
+ return snprintf(buf, buf_size, "%s %s %s",
+ llama_model_arch_name(model->arch),
+ llama_model_type_name(model->type),
+ llama_model_ftype_name(model->ftype).c_str());
+}
+
+uint64_t llama_model_size(const struct llama_model * model) {
+ uint64_t size = 0;
+ for (const auto & it : model->tensors_by_name) {
+ size += ggml_nbytes(it.second);
+ }
+ return size;
+}
+
+uint64_t llama_model_n_params(const struct llama_model * model) {
+ uint64_t nparams = 0;
+ for (const auto & it : model->tensors_by_name) {
+ nparams += ggml_nelements(it.second);
+ }
+ return nparams;
+}
+
+struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name) {
+ auto it = std::find_if(model->tensors_by_name.begin(), model->tensors_by_name.end(),
+ [name](const std::pair<std::string, struct ggml_tensor *> & it) {
+ return it.first == name;
+ });
+ if (it == model->tensors_by_name.end()) {
+ return nullptr;
+ }
+ return it->second;
+}
+
+bool llama_model_has_encoder(const struct llama_model * model) {
+ switch (model->arch) {
+ case LLM_ARCH_T5: return true;
+ default: return false;
+ }
+}
+
+llama_token llama_model_decoder_start_token(const struct llama_model * model) {
+ return model->hparams.dec_start_token_id;
+}
+
+uint32_t llama_model_quantize(
+ const char * fname_inp,
+ const char * fname_out,
+ const llama_model_quantize_params * params) {
+ try {
+ llama_model_quantize_internal(fname_inp, fname_out, params);
+ return 0;
+ } catch (const std::exception & err) {
+ LLAMA_LOG_ERROR("%s: failed to quantize: %s\n", __func__, err.what());
+ return 1;
+ }
+}
+
+struct llama_lora_adapter * llama_lora_adapter_init(struct llama_model * model, const char * path_lora) {
+ try {
+ struct llama_lora_adapter * adapter = new llama_lora_adapter(model);
+ llama_lora_adapter_init_internal(model, path_lora, *adapter);
+ return adapter;
+ } catch (const std::exception & err) {
+ LLAMA_LOG_ERROR("%s: failed to apply lora adapter: %s\n", __func__, err.what());
+ return nullptr;
+ }
+}
+
+static bool llama_control_vector_init(struct llama_control_vector & cvec, const llama_model & model) {
+ GGML_ASSERT(cvec.tensors.empty());
+ GGML_ASSERT(cvec.ctxs.empty());
+ GGML_ASSERT(cvec.bufs.empty());
+
+ // count layer buffer types
+ std::map<ggml_backend_buffer_type_t, int> buft_layer_count;
+ for (int64_t i = 0; i < model.hparams.n_layer; i++) {
+ buft_layer_count[model.buft_layer[i].buft]++;
+ }
+
+ // allocate contexts
+ std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
+ for (auto & it : buft_layer_count) {
+ int n_layers = it.second;
+ struct ggml_init_params params = {
+ /*.mem_size =*/ n_layers * ggml_tensor_overhead(),
+ /*.mem_buffer =*/ NULL,
+ /*.no_alloc =*/ true,
+ };
+ ggml_context * ctx = ggml_init(params);
+ if (!ctx) {
+ LLAMA_LOG_ERROR("%s: failed to allocate context for control vector\n", __func__);
+ return 1;
+ }
+ ctx_map[it.first] = ctx;
+ }
+
+ // make tensors
+ cvec.tensors.reserve(model.hparams.n_layer);
+ cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0
+ for (size_t il = 1; il < model.hparams.n_layer; il++) {
+ struct ggml_context * ctx = ctx_map.at(model.buft_layer[il].buft);
+ ggml_tensor * tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, model.hparams.n_embd);
+ cvec.tensors.push_back(tensor);
+ }
+
+ // allocate tensors / buffers and zero
+ cvec.ctxs.reserve(ctx_map.size());
+ cvec.bufs.reserve(ctx_map.size());
+ for (auto it : ctx_map) {
+ ggml_backend_buffer_type_t buft = it.first;
+ ggml_context * ctx = it.second;
+ ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
+ if (!buf) {
+ LLAMA_LOG_ERROR("%s: failed to allocate buffer for control vector\n", __func__);
+ return false;
+ }
+ ggml_backend_buffer_clear(buf, 0);
+ cvec.ctxs.push_back(ctx);
+ cvec.bufs.push_back(buf);
+ }
+
+ return true;
+}
+
+int32_t llama_control_vector_apply(struct llama_context * lctx, const float * data, size_t len, int32_t n_embd, int32_t il_start, int32_t il_end) {
+ const llama_model & model = lctx->model;
+ llama_control_vector & cvec = lctx->cvec;
+
+ if (data == nullptr) {
+ // disable the current control vector (but leave allocated for later)
+ cvec.layer_start = -1;
+ cvec.layer_end = -1;
+ return 0;
+ }
+
+ if (n_embd != (int) model.hparams.n_embd) {
+ LLAMA_LOG_ERROR("%s: control vector n_embd does not match model\n", __func__);
+ return 1;
+ }
+
+ if (cvec.tensors.empty()) {
+ if (!llama_control_vector_init(cvec, model)) {
+ return 1;
+ }
+ }
+
+ cvec.layer_start = il_start;
+ cvec.layer_end = il_end;
+
+ for (size_t il = 1; il < model.hparams.n_layer; il++) {
+ assert(cvec.tensors[il] != nullptr);
+
+ const size_t off = n_embd * (il - 1); // buffer doesn't have data for layer 0, since it's never present
+ if (off + n_embd <= len) {
+ ggml_backend_tensor_set(cvec.tensors[il], data + off, 0, n_embd * ggml_element_size(cvec.tensors[il]));
+ }
+ }
+
+ return 0;
+}
+
+struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max) {
+ struct llama_kv_cache_view result = {
+ /*.n_cells = */ 0,
+ /*.n_seq_max = */ n_seq_max,
+ /*.token_count = */ 0,
+ /*.used_cells = */ llama_get_kv_cache_used_cells(ctx),
+ /*.max_contiguous = */ 0,
+ /*.max_contiguous_idx = */ -1,
+ /*.cells = */ nullptr,
+ /*.cells_sequences = */ nullptr,
+ };
+ return result;
+}
+
+void llama_kv_cache_view_free(struct llama_kv_cache_view * view) {
+ if (view->cells != nullptr) {
+ free(view->cells);
+ view->cells = nullptr;
+ }
+ if (view->cells_sequences != nullptr) {
+ free(view->cells_sequences);
+ view->cells_sequences = nullptr;
+ }
+}
+
+void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view) {
+ if (uint32_t(view->n_cells) < ctx->kv_self.size || view->cells == nullptr) {
+ view->n_cells = int32_t(ctx->kv_self.size);
+ void * p = realloc(view->cells, sizeof(struct llama_kv_cache_view_cell) * view->n_cells);
+ GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells");
+ view->cells = (struct llama_kv_cache_view_cell *)p;
+ p = realloc(view->cells_sequences, sizeof(llama_seq_id) * view->n_seq_max * view->n_cells);
+ GGML_ASSERT(p != nullptr && "Failed to alloc kv_cache_view cells sequences");
+ view->cells_sequences = (llama_seq_id *)p;
+ }
+
+ const std::vector<llama_kv_cell> & kv_cells = ctx->kv_self.cells;
+ llama_kv_cache_view_cell * c_curr = view->cells;
+ llama_seq_id * cs_curr = view->cells_sequences;
+ int32_t used_cells = 0;
+ int32_t token_count = 0;
+ int32_t curr_contig_idx = -1;
+ uint32_t max_contig = 0;
+ int32_t max_contig_idx = -1;
+
+ for (int32_t i = 0; i < int32_t(ctx->kv_self.size); i++, c_curr++, cs_curr += view->n_seq_max) {
+ const size_t curr_size = kv_cells[i].seq_id.size();
+ token_count += curr_size;
+ c_curr->pos = kv_cells[i].pos + kv_cells[i].delta;
+
+ if (curr_size > 0) {
+ if (curr_contig_idx >= 0 && uint32_t(i - curr_contig_idx) > max_contig) {
+ max_contig = i - curr_contig_idx;
+ max_contig_idx = curr_contig_idx;
+ }
+ curr_contig_idx = -1;
+ } else if (curr_contig_idx < 0) {
+ curr_contig_idx = i;
+ }
+
+ int seq_idx = 0;
+ for (const llama_seq_id it : kv_cells[i].seq_id) {
+ if (seq_idx >= view->n_seq_max) {
+ break;
+ }
+ cs_curr[seq_idx] = it;
+ seq_idx++;
+ }
+ if (seq_idx != 0) {
+ used_cells++;
+ }
+ for (; seq_idx < view->n_seq_max; seq_idx++) {
+ cs_curr[seq_idx] = -1;
+ }
+ }
+ if (curr_contig_idx >= 0 && kv_cells.size() - curr_contig_idx > max_contig) {
+ max_contig_idx = curr_contig_idx;
+ max_contig = kv_cells.size() - curr_contig_idx;
+ }
+ view->max_contiguous = max_contig;
+ view->max_contiguous_idx = max_contig_idx;
+ view->token_count = token_count;
+ view->used_cells = used_cells;
+ if (uint32_t(used_cells) != ctx->kv_self.used) {
+ LLAMA_LOG_ERROR("%s: used cells mismatch. kv_cache says %d but we calculated %d\n",
+ __func__, ctx->kv_self.used, used_cells);
+ }
+}
+
+int32_t llama_get_kv_cache_token_count(const struct llama_context * ctx) {
+ int result = 0;
+
+ for (uint32_t i = 0; i < ctx->kv_self.size; i++) {
+ result += ctx->kv_self.cells[i].seq_id.size();
+ }
+
+ return result;
+}
+
+int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx) {
+ return ctx->kv_self.used;
+}
+
+void llama_kv_cache_clear(struct llama_context * ctx) {
+ llama_kv_cache_clear(ctx->kv_self);
+}
+
+bool llama_kv_cache_seq_rm(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1) {
+ return llama_kv_cache_seq_rm(ctx->kv_self, seq_id, p0, p1);
+}
+
+void llama_kv_cache_seq_cp(struct llama_context * ctx, llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) {
+ if (seq_id_src == seq_id_dst) {
+ return;
+ }
+ llama_kv_cache_seq_cp(ctx->kv_self, seq_id_src, seq_id_dst, p0, p1);
+}
+
+void llama_kv_cache_seq_keep(struct llama_context * ctx, llama_seq_id seq_id) {
+ llama_kv_cache_seq_keep(ctx->kv_self, seq_id);
+}
+
+void llama_kv_cache_seq_add(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos delta) {
+ if (delta == 0) {
+ return;
+ }
+
+ llama_kv_cache_seq_add(ctx->kv_self, seq_id, p0, p1, delta);
+}
+
+void llama_kv_cache_seq_div(struct llama_context * ctx, llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) {
+ if (d == 1) {
+ return;
+ }
+
+ llama_kv_cache_seq_div(ctx->kv_self, seq_id, p0, p1, d);
+}
+
+llama_pos llama_kv_cache_seq_pos_max(struct llama_context * ctx, llama_seq_id seq_id) {
+ return llama_kv_cache_seq_pos_max(ctx->kv_self, seq_id);
+}
+
+void llama_kv_cache_defrag(struct llama_context * ctx) {
+ llama_kv_cache_defrag(ctx->kv_self);
+}
+
+void llama_kv_cache_update(struct llama_context * ctx) {
+ llama_kv_cache_update_internal(*ctx);
+}
+
+// deprecated
+size_t llama_get_state_size(const struct llama_context * ctx) {
+ return llama_state_get_size(ctx);
+}
+
+// deprecated
+size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) {
+ return llama_state_get_data(ctx, dst);
+}
+
+// deprecated
+size_t llama_set_state_data(struct llama_context * ctx, const uint8_t * src) {
+ return llama_state_set_data(ctx, src);
+}
+
+// deprecated
+bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+ return llama_state_load_file(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
+}
+
+// deprecated
+bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
+ return llama_state_save_file(ctx, path_session, tokens, n_token_count);
+}
+
+// Returns the *maximum* size of the state
+size_t llama_state_get_size(const struct llama_context * ctx) {
+ const auto & cparams = ctx->cparams;
+ const auto & hparams = ctx->model.hparams;
+
+ // we don't know size of rng until we actually serialize it. so reserve more than enough memory for its serialized state.
+ // for reference, std::mt19937(1337) serializes to 6701 bytes.
+ const size_t s_rng_size = sizeof(size_t);
+ const size_t s_rng = LLAMA_MAX_RNG_STATE;
+ const size_t s_n_outputs = sizeof(size_t);
+ // assume worst case for outputs although only currently set ones are serialized
+ const size_t s_output_pos = ctx->cparams.n_batch * sizeof(int32_t);
+ const size_t s_logits_size = sizeof(size_t);
+ const size_t s_logits = ctx->logits_size ? cparams.n_batch * hparams.n_vocab * sizeof(float) : 0;
+ const size_t s_embedding_size = sizeof(size_t);
+ const size_t s_embedding = ctx->embd_size ? cparams.n_batch * hparams.n_embd * sizeof(float) : 0;
+ const size_t s_kv_buf_size = sizeof(size_t);
+ const size_t s_kv_head = sizeof(uint32_t);
+ const size_t s_kv_size = sizeof(uint32_t);
+ const size_t s_kv_used = sizeof(uint32_t);
+ const size_t s_v_trans = sizeof(uint32_t);
+ const size_t s_kv = ctx->kv_self.total_size();
+ const size_t s_kv_cell = sizeof(llama_pos) + sizeof(size_t) + cparams.n_seq_max*sizeof(llama_seq_id);
+ const size_t s_kv_cells = ctx->kv_self.size * s_kv_cell;
+
+ const size_t s_total = (
+ + s_rng_size
+ + s_rng
+ + s_n_outputs
+ + s_output_pos
+ + s_logits_size
+ + s_logits
+ + s_embedding_size
+ + s_embedding
+ + s_kv_buf_size
+ + s_kv_head
+ + s_kv_size
+ + s_kv_used
+ + s_v_trans
+ + s_kv
+ + s_kv_cells
+ );
+
+ // on session change it is very likely that the state size has changed - so we need to update this function
+ static_assert(LLAMA_SESSION_VERSION == 7, "So you just bumped the session version - good. But did you remember to update llama_state_get_size?");
+
+ return s_total;
+}
+
+// llama_context_data
+struct llama_data_context {
+ virtual void write(const void * src, size_t size) = 0;
+ virtual size_t get_size_written() = 0;
+ virtual ~llama_data_context() = default;
+};
+
+struct llama_data_buffer_context : llama_data_context {
+ uint8_t * ptr;
+ size_t size_written = 0;
+
+ llama_data_buffer_context(uint8_t * p) : ptr(p) {}
+
+ void write(const void * src, size_t size) override {
+ memcpy(ptr, src, size);
+ ptr += size;
+ size_written += size;
+ }
+
+ size_t get_size_written() override {
+ return size_written;
+ }
+};
+
+struct llama_data_file_context : llama_data_context {
+ llama_file * file;
+ size_t size_written = 0;
+
+ llama_data_file_context(llama_file * f) : file(f) {}
+
+ void write(const void * src, size_t size) override {
+ file->write_raw(src, size);
+ size_written += size;
+ }
+
+ size_t get_size_written() override {
+ return size_written;
+ }
+};
+
+/** copy state data into either a buffer or file depending on the passed in context
+ *
+ * file context:
+ * llama_file file("/path", "wb");
+ * llama_data_file_context data_ctx(&file);
+ * llama_state_get_data(ctx, &data_ctx);
+ *
+ * buffer context:
+ * std::vector<uint8_t> buf(max_size, 0);
+ * llama_data_buffer_context data_ctx(&buf.data());
+ * llama_state_get_data(ctx, &data_ctx);
+ *
+*/
+static void llama_state_get_data_internal(struct llama_context * ctx, llama_data_context * data_ctx) {
+ llama_synchronize(ctx);
+
+ // copy rng
+ {
+ std::ostringstream rng_ss;
+ rng_ss << ctx->sampling.rng;
+
+ const std::string & rng_str = rng_ss.str();
+ const size_t rng_size = rng_str.size();
+
+ GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE);
+
+ data_ctx->write(&rng_size, sizeof(rng_size));
+ data_ctx->write(rng_str.data(), rng_size);
+ }
+
+ // copy outputs
+ {
+ // Can't use ctx->n_outputs because it's not for the
+ // entire last batch when n_ubatch is smaller than n_batch
+ size_t n_outputs = 0;
+
+ // copy output ids
+ {
+ std::vector<int32_t> output_pos;
+
+ const size_t n_batch = ctx->cparams.n_batch;
+ const auto & output_ids = ctx->output_ids;
+
+ output_pos.resize(ctx->output_size);
+
+ // build a more compact representation of the output ids
+ for (size_t i = 0; i < n_batch; ++i) {
+ // map an output id to a position in the batch
+ int32_t pos = output_ids[i];
+ if (pos >= 0) {
+ if ((size_t) pos >= n_outputs) {
+ n_outputs = pos + 1;
+ }
+ GGML_ASSERT((size_t) pos < ctx->output_size);
+ output_pos[pos] = i;
+ }
+ }
+
+ data_ctx->write(&n_outputs, sizeof(n_outputs));
+
+ if (n_outputs) {
+ data_ctx->write(output_pos.data(), n_outputs * sizeof(int32_t));
+ }
+ }
+
+ // copy logits
+ {
+ const size_t logits_size = std::min(ctx->logits_size, n_outputs * ctx->model.hparams.n_vocab);
+
+ data_ctx->write(&logits_size, sizeof(logits_size));
+
+ if (logits_size) {
+ data_ctx->write(ctx->logits, logits_size * sizeof(float));
+ }
+ }
+
+ // copy embeddings
+ {
+ const size_t embeddings_size = std::min(ctx->embd_size, n_outputs * ctx->model.hparams.n_embd);
+
+ data_ctx->write(&embeddings_size, sizeof(embeddings_size));
+
+ if (embeddings_size) {
+ data_ctx->write(ctx->embd, embeddings_size * sizeof(float));
+ }
+ }
+ }
+
+ // copy kv cache
+ {
+ const auto & kv_self = ctx->kv_self;
+ const auto & hparams = ctx->model.hparams;
+
+ const uint32_t n_layer = hparams.n_layer;
+
+ // NOTE: kv_size and kv_buf_size are mostly used for sanity checks
+ const uint32_t kv_head = llama_kv_cache_cell_max(kv_self);
+ const uint32_t kv_size = kv_self.size;
+ const size_t kv_buf_size = kv_self.total_size() / (kv_size ? kv_size : 1) * kv_head;
+ const uint32_t kv_used = kv_self.used;
+ const uint32_t v_trans = kv_self.v_trans ? 1 : 0;
+
+ data_ctx->write(&kv_buf_size, sizeof(kv_buf_size));
+ data_ctx->write(&kv_head, sizeof(kv_head));
+ data_ctx->write(&kv_size, sizeof(kv_size));
+ data_ctx->write(&kv_used, sizeof(kv_used));
+ data_ctx->write(&v_trans, sizeof(v_trans));
+
+ if (kv_buf_size) {
+ const size_t pre_kv_buf_size = data_ctx->get_size_written();
+
+ std::vector<uint8_t> tmp_buf;
+ for (int il = 0; il < (int) n_layer; ++il) {
+ const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
+ const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
+
+ const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
+
+ tmp_buf.resize(k_size);
+ ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), 0, tmp_buf.size());
+ data_ctx->write(tmp_buf.data(), tmp_buf.size());
+
+ if (kv_self.recurrent || !kv_self.v_trans) {
+ // v is contiguous for recurrent models
+ // TODO: use other tensors for state models than k and v
+ const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head);
+
+ tmp_buf.resize(v_size);
+ ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), 0, tmp_buf.size());
+ data_ctx->write(tmp_buf.data(), tmp_buf.size());
+ continue;
+ }
+
+ // v is not contiguous, copy row by row
+ const size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head);
+ const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_size);
+
+ tmp_buf.resize(v_row_size);
+ for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) {
+ ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), ir*v_row_stride, tmp_buf.size());
+ data_ctx->write(tmp_buf.data(), tmp_buf.size());
+ }
+ }
+ GGML_ASSERT(kv_buf_size == data_ctx->get_size_written() - pre_kv_buf_size);
+ }
+
+ for (uint32_t i = 0; i < kv_head; ++i) {
+ const auto & cell = kv_self.cells[i];
+
+ const llama_pos pos = cell.pos;
+ const size_t seq_id_size = cell.seq_id.size();
+
+ data_ctx->write(&pos, sizeof(pos));
+ data_ctx->write(&seq_id_size, sizeof(seq_id_size));
+
+ for (auto seq_id : cell.seq_id) {
+ data_ctx->write(&seq_id, sizeof(seq_id));
+ }
+ }
+ }
+}
+
+size_t llama_state_get_data(struct llama_context * ctx, uint8_t * dst) {
+ llama_data_buffer_context data_ctx(dst);
+ llama_state_get_data_internal(ctx, &data_ctx);
+
+ return data_ctx.get_size_written();
+}
+
+// Sets the state reading from the specified source address
+size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) {
+ llama_synchronize(ctx);
+
+ const uint8_t * inp = src;
+
+ // set rng
+ {
+ size_t rng_size;
+ memcpy(&rng_size, inp, sizeof(rng_size)); inp += sizeof(rng_size);
+
+ GGML_ASSERT(rng_size <= LLAMA_MAX_RNG_STATE);
+
+ std::string rng_str((const char *)inp, rng_size); inp += rng_size;
+
+ std::istringstream rng_ss(rng_str);
+ rng_ss >> ctx->sampling.rng;
+
+ GGML_ASSERT(!rng_ss.fail());
+ }
+
+ // set output ids
+ {
+ size_t n_outputs;
+ std::vector<int32_t> output_pos;
+
+ memcpy(&n_outputs, inp, sizeof(n_outputs)); inp += sizeof(n_outputs);
+
+ GGML_ASSERT(n_outputs <= llama_output_reserve(*ctx, n_outputs));
+
+ if (n_outputs) {
+ output_pos.resize(n_outputs);
+ memcpy(output_pos.data(), inp, n_outputs * sizeof(int32_t));
+ inp += n_outputs * sizeof(int32_t);
+
+ for (int32_t i = 0; i < (int32_t) output_pos.size(); ++i) {
+ int32_t id = output_pos[i];
+ GGML_ASSERT((uint32_t) id < ctx->cparams.n_batch);
+ ctx->output_ids[id] = i;
+ }
+
+ ctx->n_outputs = n_outputs;
+ }
+ }
+
+ // set logits
+ {
+ size_t logits_size;
+
+ memcpy(&logits_size, inp, sizeof(logits_size)); inp += sizeof(logits_size);
+
+ GGML_ASSERT(ctx->logits_size >= logits_size);
+
+ if (logits_size) {
+ memcpy(ctx->logits, inp, logits_size * sizeof(float));
+ inp += logits_size * sizeof(float);
+ }
+ }
+
+ // set embeddings
+ {
+ size_t embeddings_size;
+
+ memcpy(&embeddings_size, inp, sizeof(embeddings_size)); inp += sizeof(embeddings_size);
+
+ GGML_ASSERT(ctx->embd_size >= embeddings_size);
+
+ if (embeddings_size) {
+ memcpy(ctx->embd, inp, embeddings_size * sizeof(float));
+ inp += embeddings_size * sizeof(float);
+ }
+ }
+
+ // set kv cache
+ {
+ const auto & kv_self = ctx->kv_self;
+ const auto & hparams = ctx->model.hparams;
+
+ const uint32_t n_layer = hparams.n_layer;
+
+ size_t kv_buf_size;
+ uint32_t kv_head;
+ uint32_t kv_size;
+ uint32_t kv_used;
+ uint32_t v_trans;
+
+ memcpy(&kv_buf_size, inp, sizeof(kv_buf_size)); inp += sizeof(kv_buf_size);
+ memcpy(&kv_head, inp, sizeof(kv_head)); inp += sizeof(kv_head);
+ memcpy(&kv_size, inp, sizeof(kv_size)); inp += sizeof(kv_size);
+ memcpy(&kv_used, inp, sizeof(kv_used)); inp += sizeof(kv_used);
+ memcpy(&v_trans, inp, sizeof(v_trans)); inp += sizeof(v_trans);
+
+ GGML_ASSERT(kv_self.v_trans == (bool) v_trans); // incompatible V transposition
+
+ if (kv_self.size != kv_size) {
+ // the KV cache needs to be big enough to load all the KV cells from the saved state
+ GGML_ASSERT(kv_self.size >= kv_head);
+
+ LLAMA_LOG_INFO("%s: state contains %d KV cells, was saved with kv_size=%d, but is loaded with kv_size=%d (fine, but different)\n",
+ __func__, kv_head, kv_size, kv_self.size);
+ }
+
+ llama_kv_cache_clear(ctx);
+
+ if (kv_buf_size) {
+ const size_t pre_kv_buf_size = inp - src;
+
+ GGML_ASSERT(kv_self.total_size() >= kv_buf_size);
+
+ for (int il = 0; il < (int) n_layer; ++il) {
+ const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
+ const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
+
+ const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
+
+ ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size);
+ inp += k_size;
+
+ if (kv_self.recurrent || !kv_self.v_trans) {
+ // v is contiguous for recurrent models
+ // TODO: use other tensors for state models than k and v
+ const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head);
+
+ ggml_backend_tensor_set(kv_self.v_l[il], inp, 0, v_size);
+ inp += v_size;
+ continue;
+ }
+
+ // v is not contiguous, copy row by row
+ const size_t v_row_size = ggml_row_size(kv_self.v_l[il]->type, kv_head);
+ const size_t v_row_stride = ggml_row_size(kv_self.v_l[il]->type, kv_self.size);
+
+ for (int ir = 0; ir < (int) n_embd_v_gqa; ++ir) {
+ ggml_backend_tensor_set(kv_self.v_l[il], inp, ir*v_row_stride, v_row_size);
+ inp += v_row_size;
+ }
+ }
+ GGML_ASSERT(kv_buf_size == inp - src - pre_kv_buf_size);
+ }
+
+ ctx->kv_self.head = kv_head;
+ ctx->kv_self.used = kv_used;
+
+ for (uint32_t i = 0; i < kv_head; ++i) {
+ llama_pos pos;
+ size_t seq_id_size;
+
+ memcpy(&pos, inp, sizeof(pos)); inp += sizeof(pos);
+ memcpy(&seq_id_size, inp, sizeof(seq_id_size)); inp += sizeof(seq_id_size);
+
+ ctx->kv_self.cells[i].pos = pos;
+
+ llama_seq_id seq_id;
+
+ for (size_t j = 0; j < seq_id_size; ++j) {
+ memcpy(&seq_id, inp, sizeof(seq_id)); inp += sizeof(seq_id);
+ ctx->kv_self.cells[i].seq_id.insert(seq_id);
+ }
+ }
+ }
+
+ const size_t nread = inp - src;
+ const size_t max_size = llama_state_get_size(ctx);
+
+ GGML_ASSERT(nread <= max_size);
+
+ return nread;
+}
+
+static bool llama_state_load_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+ llama_file file(path_session, "rb");
+
+ // sanity checks
+ {
+ const uint32_t magic = file.read_u32();
+ const uint32_t version = file.read_u32();
+
+ if (magic != LLAMA_SESSION_MAGIC || version != LLAMA_SESSION_VERSION) {
+ LLAMA_LOG_ERROR("%s : unknown (magic, version) for session file: %08x, %08x\n", __func__, magic, version);
+ return false;
+ }
+
+ llama_hparams session_hparams;
+ file.read_raw(&session_hparams, sizeof(llama_hparams));
+
+ if (session_hparams != ctx->model.hparams) {
+ LLAMA_LOG_INFO("%s : model hparams didn't match from session file!\n", __func__);
+ return false;
+ }
+ }
+
+ // load the prompt
+ {
+ const uint32_t n_token_count = file.read_u32();
+
+ if (n_token_count > n_token_capacity) {
+ LLAMA_LOG_ERROR("%s : token count in session file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
+ return false;
+ }
+
+ file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
+ *n_token_count_out = n_token_count;
+ }
+
+ // restore the context state
+ {
+ const size_t n_state_size_cur = file.size - file.tell();
+ const size_t n_state_size_max = llama_state_get_size(ctx);
+
+ if (n_state_size_cur > n_state_size_max) {
+ LLAMA_LOG_ERROR("%s : the state size in session file is too big! max %zu, got %zu\n", __func__, n_state_size_max, n_state_size_cur);
+ return false;
+ }
+
+ std::vector<uint8_t> state_data(n_state_size_max);
+ file.read_raw(state_data.data(), n_state_size_cur);
+
+ llama_state_set_data(ctx, state_data.data());
+ }
+
+ return true;
+}
+
+bool llama_state_load_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+ try {
+ return llama_state_load_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out);
+ } catch (const std::exception & err) {
+ LLAMA_LOG_ERROR("error loading session file: %s\n", err.what());
+ return false;
+ }
+}
+
+static bool llama_state_save_file_internal(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
+ llama_file file(path_session, "wb");
+
+ file.write_u32(LLAMA_SESSION_MAGIC);
+ file.write_u32(LLAMA_SESSION_VERSION);
+
+ file.write_raw(&ctx->model.hparams, sizeof(llama_hparams));
+
+ // save the prompt
+ file.write_u32((uint32_t) n_token_count);
+ file.write_raw(tokens, sizeof(llama_token) * n_token_count);
+
+ // save the context state using stream saving
+ llama_data_file_context data_ctx(&file);
+ llama_state_get_data_internal(ctx, &data_ctx);
+
+ return true;
+}
+
+bool llama_state_save_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) {
+ try {
+ return llama_state_save_file_internal(ctx, path_session, tokens, n_token_count);
+ } catch (const std::exception & err) {
+ LLAMA_LOG_ERROR("error saving session file: %s\n", err.what());
+ return false;
+ }
+}
+
+size_t llama_state_seq_get_size(struct llama_context* ctx, llama_seq_id seq_id) {
+ // save the size of size_t as a uint32_t for safety check
+ const size_t size_t_size_size = sizeof(uint32_t);
+
+ // other values
+ const size_t s_cell_count_size = sizeof(uint32_t);
+ const size_t s_layer_count_size = sizeof(uint32_t);
+ const size_t n_embd_v_gqa_size = sizeof(uint32_t);
+
+ size_t s_cell_count = 0;
+ size_t s_cell_data_size = 0;
+ const auto & kv_self = ctx->kv_self;
+ const auto & hparams = ctx->model.hparams;
+
+ const uint32_t n_layer = hparams.n_layer;
+
+ for (uint32_t i = 0; i < kv_self.size; ++i) {
+ const auto & cell = kv_self.cells[i];
+ if (cell.seq_id.count(seq_id) > 0) {
+ ++s_cell_count;
+ s_cell_data_size += sizeof(llama_pos);
+ }
+ }
+
+ for (int il = 0; il < (int)n_layer; ++il) {
+ const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
+ const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
+
+ // types of keys and values
+ s_cell_data_size += sizeof(int32_t) * 2;
+ // k_size_row and v_size_el values of layer
+ s_cell_data_size += sizeof(size_t) * 2;
+
+ // keys
+ const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
+ s_cell_data_size += k_size_row * s_cell_count;
+
+ // values (transposed)
+ const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
+ s_cell_data_size += v_size_el * s_cell_count * n_embd_v_gqa;
+ }
+
+ const size_t s_total = (
+ size_t_size_size +
+ s_cell_count_size +
+ s_layer_count_size +
+ n_embd_v_gqa_size +
+ s_cell_data_size
+ );
+
+ return s_total;
+}
+
+static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llama_data_context & data_ctx, llama_seq_id seq_id) {
+ llama_synchronize(ctx);
+
+ const auto & kv_self = ctx->kv_self;
+ GGML_ASSERT(!kv_self.recurrent); // not implemented
+
+ // Save the size of size_t as a uint32_t for safety check
+ const uint32_t size_t_size = sizeof(size_t);
+ data_ctx.write(&size_t_size, sizeof(size_t_size));
+
+ std::vector<std::pair<uint32_t, uint32_t>> cell_ranges; // ranges, from inclusive, to exclusive
+ uint32_t cell_count = 0;
+
+ // Count the number of cells with the specified seq_id
+ // Find all the ranges of cells with this seq id
+ {
+ uint32_t cell_range_begin = kv_self.size;
+ for (uint32_t i = 0; i < kv_self.size; ++i) {
+ const auto & cell = kv_self.cells[i];
+ if (cell.has_seq_id(seq_id)) {
+ ++cell_count;
+ if (cell_range_begin == kv_self.size) {
+ cell_range_begin = i;
+ }
+ }
+ else {
+ if (cell_range_begin != kv_self.size) {
+ cell_ranges.emplace_back(cell_range_begin, i);
+ cell_range_begin = kv_self.size;
+ }
+ }
+ }
+ if (cell_range_begin != kv_self.size) {
+ cell_ranges.emplace_back(cell_range_begin, kv_self.size);
+ }
+
+ // DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count
+ uint32_t cell_count_check = 0;
+ for (const auto & range : cell_ranges) {
+ cell_count_check += range.second - range.first;
+ }
+ GGML_ASSERT(cell_count == cell_count_check);
+ }
+
+ // Write the cell count
+ data_ctx.write(&cell_count, sizeof(cell_count));
+
+ const auto & hparams = ctx->model.hparams;
+ const uint32_t n_layer = hparams.n_layer;
+
+ // Write the layer count
+ data_ctx.write(&n_layer, sizeof(n_layer));
+
+ // Write n_embd_v_gqa (reference value)
+ {
+ const uint32_t n_embd_v_gqa_ref = hparams.n_embd_v_gqa() + hparams.n_embd_k_s();
+ data_ctx.write(&n_embd_v_gqa_ref, sizeof(n_embd_v_gqa_ref));
+ }
+
+ // Iterate the ranges and write all the pos (this is the token position in the prompt)
+ for (const auto & range : cell_ranges) {
+ for (uint32_t i = range.first; i < range.second; ++i) {
+ const auto & cell = kv_self.cells[i];
+ data_ctx.write(&cell.pos, sizeof(cell.pos));
+ }
+ }
+
+ // Iterate and write all the keys first, each row is a cell
+ // Get whole range at a time
+ std::vector<uint8_t> tmp_buf;
+ for (int il = 0; il < (int)n_layer; ++il) {
+ const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
+
+ // Write key type
+ const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type;
+ data_ctx.write(&k_type_i, sizeof(k_type_i));
+
+ // Write row size of key
+ const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
+ data_ctx.write(&k_size_row, sizeof(k_size_row));
+
+ // Read each range of cells of k_size length each into tmp_buf and write out
+ for (const auto & range : cell_ranges) {
+ const size_t range_size = range.second - range.first;
+ tmp_buf.resize(range_size * k_size_row);
+ ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), range.first * k_size_row, range_size * k_size_row);
+ data_ctx.write(tmp_buf.data(), tmp_buf.size());
+ }
+ }
+
+ // TODO: simplify, reduce copy-paste
+ if (!kv_self.v_trans) {
+ for (int il = 0; il < (int)n_layer; ++il) {
+ const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
+
+ // Write value type
+ const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+ data_ctx.write(&v_type_i, sizeof(v_type_i));
+
+ // Write row size of value
+ const size_t v_size_row = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa);
+ data_ctx.write(&v_size_row, sizeof(v_size_row));
+
+ // Read each range of cells of v_size length each into tmp_buf and write out
+ for (const auto & range : cell_ranges) {
+ const size_t range_size = range.second - range.first;
+ tmp_buf.resize(range_size * v_size_row);
+ ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), range.first * v_size_row, range_size * v_size_row);
+ data_ctx.write(tmp_buf.data(), tmp_buf.size());
+ }
+ }
+ } else {
+ // For the values, they are transposed, so we also need the element size and get the element ranges from each row
+ const uint32_t kv_size = kv_self.size;
+ for (int il = 0; il < (int)n_layer; ++il) {
+ const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
+
+ // Write value type
+ const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+ data_ctx.write(&v_type_i, sizeof(v_type_i));
+
+ // Write element size
+ const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
+ data_ctx.write(&v_size_el, sizeof(v_size_el));
+
+ // For each row, we get the element values of each cell
+ for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+ // Read each range of cells of v_size_el length each into tmp_buf and write out
+ for (const auto & range : cell_ranges) {
+ const size_t range_size = range.second - range.first;
+ const size_t src_offset = (range.first + j * kv_size) * v_size_el;
+ tmp_buf.resize(range_size * v_size_el);
+ ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), src_offset, tmp_buf.size());
+ data_ctx.write(tmp_buf.data(), tmp_buf.size());
+ }
+ }
+ }
+ }
+
+ return data_ctx.get_size_written();
+}
+
+size_t llama_state_seq_get_data(struct llama_context* ctx, uint8_t* dst, llama_seq_id seq_id) {
+ llama_data_buffer_context data_ctx(dst);
+ return llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
+}
+
+size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src, llama_seq_id dest_seq_id) {
+ llama_synchronize(ctx);
+
+ auto & kv_self = ctx->kv_self;
+ GGML_ASSERT(!kv_self.recurrent); // not implemented
+
+ // Wipe the slot
+ llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+
+ const uint8_t * inp = src;
+
+ // Read size of size_t
+ uint32_t size_t_size;
+ memcpy(&size_t_size, inp, sizeof(size_t_size));
+ inp += sizeof(size_t_size);
+ if (size_t_size != sizeof(size_t)) {
+ LLAMA_LOG_ERROR("%s: size_t size mismatch\n", __func__);
+ return 0;
+ }
+
+ // Read the cell count
+ uint32_t cell_count;
+ memcpy(&cell_count, inp, sizeof(cell_count));
+ inp += sizeof(cell_count);
+
+ // Read the layer count
+ uint32_t n_layer_ref;
+ memcpy(&n_layer_ref, inp, sizeof(n_layer_ref));
+ inp += sizeof(n_layer_ref);
+
+ // Read n_embd_v_gqa
+ uint32_t n_embd_v_gqa_ref;
+ memcpy(&n_embd_v_gqa_ref, inp, sizeof(n_embd_v_gqa_ref));
+ inp += sizeof(n_embd_v_gqa_ref);
+
+ // Sanity check model compatibility
+ const auto & hparams = ctx->model.hparams;
+ const uint32_t n_layer = hparams.n_layer;
+
+ if (n_layer != n_layer_ref) {
+ LLAMA_LOG_ERROR("%s: mismatched n_layer (%d != %d)\n", __func__, n_layer, n_layer_ref);
+ return 0;
+ }
+
+ if (hparams.n_embd_v_gqa() != n_embd_v_gqa_ref) {
+ LLAMA_LOG_ERROR("%s: mismatched n_embd_v_gqa (%d != %d)\n", __func__, hparams.n_embd_v_gqa(), n_embd_v_gqa_ref);
+ return 0;
+ }
+
+ // Allocate the new cells for the slot
+ if (cell_count) {
+ llama_batch batch = llama_batch_init(cell_count, 0, 1);
+ batch.n_tokens = cell_count;
+ for (uint32_t i = 0; i < cell_count; ++i) {
+ llama_pos pos;
+ memcpy(&pos, inp, sizeof(pos));
+ inp += sizeof(pos);
+
+ batch.pos[i] = pos;
+ batch.n_seq_id[i] = 1;
+ batch.seq_id[i][0] = dest_seq_id;
+ }
+ if (!llama_kv_cache_find_slot(kv_self, batch)) {
+ llama_batch_free(batch);
+ LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
+ return 0;
+ }
+
+ // DEBUG CHECK: kv_self.head should be our first cell, kv_self.head + cell_count - 1 should be our last cell (verify seq_id and pos values)
+ // Assume that this is one contiguous block of cells
+ GGML_ASSERT(kv_self.head + cell_count <= kv_self.size);
+ GGML_ASSERT(kv_self.cells[kv_self.head].pos == batch.pos[0]);
+ GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].pos == batch.pos[cell_count - 1]);
+ GGML_ASSERT(kv_self.cells[kv_self.head].has_seq_id(dest_seq_id));
+ GGML_ASSERT(kv_self.cells[kv_self.head + cell_count - 1].has_seq_id(dest_seq_id));
+
+ // Cleanup
+ llama_batch_free(batch);
+ }
+
+ const uint32_t kv_size = kv_self.size;
+ const uint32_t kv_head = kv_self.head;
+
+ // For each layer, read the keys for each cell, one row is one cell, read as one contiguous blo
+ for (int il = 0; il < (int)n_layer; ++il) {
+ const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il) + hparams.n_embd_k_s();
+
+ // Read type of key
+ int32_t k_type_i_ref;
+ memcpy(&k_type_i_ref, inp, sizeof(k_type_i_ref));
+ inp += sizeof(k_type_i_ref);
+ const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type;
+ if (k_type_i != k_type_i_ref) {
+ llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+ LLAMA_LOG_ERROR("%s: mismatched key type (%d != %d, layer %d)\n", __func__, k_type_i, k_type_i_ref, il);
+ return 0;
+ }
+
+ // Read row size of key
+ size_t k_size_row_ref;
+ memcpy(&k_size_row_ref, inp, sizeof(k_size_row_ref));
+ inp += sizeof(k_size_row_ref);
+ const size_t k_size_row = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa);
+ if (k_size_row != k_size_row_ref) {
+ llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+ LLAMA_LOG_ERROR("%s: mismatched key row size (%zu != %zu, layer %d)\n", __func__, k_size_row, k_size_row_ref, il);
+ return 0;
+ }
+
+ if (cell_count) {
+ // Read and set the keys for the whole cell range
+ ggml_backend_tensor_set(kv_self.k_l[il], inp, kv_head * k_size_row, cell_count * k_size_row);
+ inp += cell_count * k_size_row;
+ }
+ }
+
+ // TODO: simplify, reduce copy-paste
+ if (!kv_self.v_trans) {
+ for (int il = 0; il < (int)n_layer; ++il) {
+ const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
+
+ // Read type of value
+ int32_t v_type_i_ref;
+ memcpy(&v_type_i_ref, inp, sizeof(v_type_i_ref));
+ inp += sizeof(v_type_i_ref);
+ const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+ if (v_type_i != v_type_i_ref) {
+ llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+ LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
+ return 0;
+ }
+
+ // Read row size of value
+ size_t v_size_row_ref;
+ memcpy(&v_size_row_ref, inp, sizeof(v_size_row_ref));
+ inp += sizeof(v_size_row_ref);
+ const size_t v_size_row = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa);
+ if (v_size_row != v_size_row_ref) {
+ llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+ LLAMA_LOG_ERROR("%s: mismatched value row size (%zu != %zu, layer %d)\n", __func__, v_size_row, v_size_row_ref, il);
+ return 0;
+ }
+
+ if (cell_count) {
+ // Read and set the values for the whole cell range
+ ggml_backend_tensor_set(kv_self.v_l[il], inp, kv_head * v_size_row, cell_count * v_size_row);
+ inp += cell_count * v_size_row;
+ }
+ }
+ } else {
+ // For each layer, read the values for each cell (transposed)
+ for (int il = 0; il < (int)n_layer; ++il) {
+ const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il) + hparams.n_embd_v_s();
+
+ // Read type of value
+ int32_t v_type_i_ref;
+ memcpy(&v_type_i_ref, inp, sizeof(v_type_i_ref));
+ inp += sizeof(v_type_i_ref);
+ const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
+ if (v_type_i != v_type_i_ref) {
+ llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+ LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il);
+ return 0;
+ }
+
+ // Read element size of value
+ size_t v_size_el_ref;
+ memcpy(&v_size_el_ref, inp, sizeof(v_size_el_ref));
+ inp += sizeof(v_size_el_ref);
+ const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type);
+ if (v_size_el != v_size_el_ref) {
+ llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1);
+ LLAMA_LOG_ERROR("%s: mismatched value element size (%zu != %zu, layer %d)\n", __func__, v_size_el, v_size_el_ref, il);
+ return 0;
+ }
+
+ if (cell_count) {
+ // For each row in the transposed matrix, read the values for the whole cell range
+ for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+ const size_t dst_offset = (kv_head + j * kv_size) * v_size_el;
+ ggml_backend_tensor_set(kv_self.v_l[il], inp, dst_offset, cell_count * v_size_el);
+ inp += cell_count * v_size_el;
+ }
+ }
+ }
+ }
+
+ const size_t nread = inp - src;
+
+ return nread;
+}
+
+static size_t llama_state_seq_save_file_internal(struct llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count) {
+ llama_file file(filepath, "wb");
+
+ file.write_u32(LLAMA_STATE_SEQ_MAGIC);
+ file.write_u32(LLAMA_STATE_SEQ_VERSION);
+
+ // save the prompt
+ file.write_u32((uint32_t)n_token_count);
+ file.write_raw(tokens, sizeof(llama_token) * n_token_count);
+
+ // save the context state using stream saving
+ llama_data_file_context data_ctx(&file);
+ llama_state_seq_get_data_internal(ctx, data_ctx, seq_id);
+
+ const size_t res = file.tell();
+ GGML_ASSERT(res == sizeof(uint32_t) * 3 + sizeof(llama_token) * n_token_count + data_ctx.get_size_written());
+ return res;
+}
+
+static size_t llama_state_seq_load_file_internal(struct llama_context * ctx, const char * filepath, llama_seq_id dest_seq_id, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+ llama_file file(filepath, "rb");
+
+ // version checks
+ {
+ const uint32_t magic = file.read_u32();
+ const uint32_t version = file.read_u32();
+
+ if (magic != LLAMA_STATE_SEQ_MAGIC || version != LLAMA_STATE_SEQ_VERSION) {
+ LLAMA_LOG_ERROR("%s: unknown (magic, version) for sequence state file: %08x, %08x\n", __func__, magic, version);
+ return 0;
+ }
+ }
+
+ // load the prompt
+ {
+ const uint32_t n_token_count = file.read_u32();
+
+ if (n_token_count > n_token_capacity) {
+ LLAMA_LOG_ERROR("%s: token count in sequence state file exceeded capacity! %u > %zu\n", __func__, n_token_count, n_token_capacity);
+ return 0;
+ }
+
+ file.read_raw(tokens_out, sizeof(llama_token) * n_token_count);
+ *n_token_count_out = n_token_count;
+ }
+
+ // restore the context state
+ {
+ const size_t state_size = file.size - file.tell();
+ std::vector<uint8_t> state_data(state_size);
+ file.read_raw(state_data.data(), state_size);
+ const size_t nread = llama_state_seq_set_data(ctx, state_data.data(), dest_seq_id);
+ if (!nread) {
+ LLAMA_LOG_ERROR("%s: failed to restore sequence state\n", __func__);
+ return 0;
+ }
+ GGML_ASSERT(nread <= state_size);
+ GGML_ASSERT(nread + sizeof(uint32_t) * 3 + sizeof(llama_token) * *n_token_count_out == file.tell());
+ }
+
+ return file.tell();
+}
+
+size_t llama_state_seq_save_file(struct llama_context * ctx, const char * filepath, llama_seq_id seq_id, const llama_token * tokens, size_t n_token_count) {
+ try {
+ return llama_state_seq_save_file_internal(ctx, filepath, seq_id, tokens, n_token_count);
+ } catch (const std::exception & err) {
+ LLAMA_LOG_ERROR("error saving sequence state file: %s\n", err.what());
+ return 0;
+ }
+}
+
+size_t llama_state_seq_load_file(struct llama_context * ctx, const char * filepath, llama_seq_id dest_seq_id, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) {
+ try {
+ return llama_state_seq_load_file_internal(ctx, filepath, dest_seq_id, tokens_out, n_token_capacity, n_token_count_out);
+ } catch (const std::exception & err) {
+ LLAMA_LOG_ERROR("error loading sequence state file: %s\n", err.what());
+ return 0;
+ }
+}
+
+void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch) {
+ ctx->cparams.n_threads = n_threads;
+ ctx->cparams.n_threads_batch = n_threads_batch;
+}
+
+uint32_t llama_n_threads(struct llama_context * ctx) {
+ return ctx->cparams.n_threads;
+}
+
+uint32_t llama_n_threads_batch(struct llama_context * ctx) {
+ return ctx->cparams.n_threads_batch;
+}
+
+void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)(void * data), void * abort_callback_data) {
+ ctx->abort_callback = abort_callback;
+ ctx->abort_callback_data = abort_callback_data;
+}
+
+void llama_set_embeddings(struct llama_context * ctx, bool embeddings) {
+ ctx->cparams.embeddings = embeddings;
+}
+
+void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn) {
+ ctx->cparams.causal_attn = causal_attn;
+}
+
+struct llama_batch llama_batch_get_one(
+ llama_token * tokens,
+ int32_t n_tokens,
+ llama_pos pos_0,
+ llama_seq_id seq_id) {
+ return {
+ /*n_tokens =*/ n_tokens,
+ /*tokens =*/ tokens,
+ /*embd =*/ nullptr,
+ /*pos =*/ nullptr,
+ /*n_seq_id =*/ nullptr,
+ /*seq_id =*/ nullptr,
+ /*logits =*/ nullptr,
+ /*all_pos_0 =*/ pos_0,
+ /*all_pos_1 =*/ 1,
+ /*all_seq_id =*/ seq_id,
+ };
+}
+
+struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_t n_seq_max) {
+ llama_batch batch = { 0, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, 0, 0, 0, };
+
+ if (embd) {
+ batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd);
+ } else {
+ batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc);
+ }
+
+ batch.pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens_alloc);
+ batch.n_seq_id = (int32_t *) malloc(sizeof(int32_t) * n_tokens_alloc);
+ batch.seq_id = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * (n_tokens_alloc + 1));
+ for (int i = 0; i < n_tokens_alloc; ++i) {
+ batch.seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
+ }
+ batch.seq_id[n_tokens_alloc] = nullptr;
+
+ batch.logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens_alloc);
+
+ return batch;
+}
+
+void llama_batch_free(struct llama_batch batch) {
+ if (batch.token) free(batch.token);
+ if (batch.embd) free(batch.embd);
+ if (batch.pos) free(batch.pos);
+ if (batch.n_seq_id) free(batch.n_seq_id);
+ if (batch.seq_id) {
+ for (int i = 0; batch.seq_id[i] != nullptr; ++i) {
+ free(batch.seq_id[i]);
+ }
+ free(batch.seq_id);
+ }
+ if (batch.logits) free(batch.logits);
+}
+
+int32_t llama_encode(
+ struct llama_context * ctx,
+ struct llama_batch batch) {
+ const int ret = llama_encode_internal(*ctx, batch);
+ if (ret < 0) {
+ LLAMA_LOG_ERROR("%s: failed to encode, ret = %d\n", __func__, ret);
+ }
+
+ return ret;
+}
+
+int32_t llama_decode(
+ struct llama_context * ctx,
+ struct llama_batch batch) {
+ const int ret = llama_decode_internal(*ctx, batch);
+ if (ret < 0) {
+ LLAMA_LOG_ERROR("%s: failed to decode, ret = %d\n", __func__, ret);
+ }
+
+ return ret;
+}
+
+void llama_synchronize(struct llama_context * ctx) {
+ ggml_backend_sched_synchronize(ctx->sched);
+
+ // FIXME: if multiple single tokens are evaluated without a synchronization,
+ // the stats will be added to the prompt evaluation stats
+ // this should only happen when using batch size 1 to evaluate a batch
+
+ // add the evaluation to the stats
+ if (ctx->n_queued_tokens == 1) {
+ ctx->t_eval_us += ggml_time_us() - ctx->t_compute_start_us;
+ ctx->n_eval++;
+ } else if (ctx->n_queued_tokens > 1) {
+ ctx->t_p_eval_us += ggml_time_us() - ctx->t_compute_start_us;
+ ctx->n_p_eval += ctx->n_queued_tokens;
+ }
+
+ // get a more accurate load time, upon first eval
+ if (ctx->n_queued_tokens > 0 && !ctx->has_evaluated_once) {
+ ctx->t_load_us = ggml_time_us() - ctx->t_start_us;
+ ctx->has_evaluated_once = true;
+ }
+
+ ctx->n_queued_tokens = 0;
+ ctx->t_compute_start_us = 0;
+}
+
+float * llama_get_logits(struct llama_context * ctx) {
+ llama_synchronize(ctx);
+
+ return ctx->logits;
+}
+
+float * llama_get_logits_ith(struct llama_context * ctx, int32_t i) {
+ int32_t j = -1;
+ llama_synchronize(ctx);
+
+ try {
+ if (ctx->logits == nullptr) {
+ throw std::runtime_error("no logits");
+ }
+
+ if (i < 0) {
+ j = ctx->n_outputs + i;
+ if (j < 0) {
+ throw std::runtime_error(format("negative index out of range [0, %d)", ctx->n_outputs));
+ }
+ } else if ((size_t) i >= ctx->output_ids.size()) {
+ throw std::runtime_error(format("out of range [0, %lu)", ctx->output_ids.size()));
+ } else {
+ j = ctx->output_ids[i];
+ }
+
+ if (j < 0) {
+ throw std::runtime_error(format("batch.logits[%d] != true", i));
+ }
+ if (j >= ctx->n_outputs) {
+ // This should not happen
+ throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, ctx->n_outputs));
+ }
+
+ return ctx->logits + j*ctx->model.hparams.n_vocab;
+ } catch (const std::exception & err) {
+ LLAMA_LOG_ERROR("%s: invalid logits id %d, reason: %s\n", __func__, i, err.what());
+#ifndef NDEBUG
+ GGML_ASSERT(false);
+#endif
+ return nullptr;
+ }
+}
+
+float * llama_get_embeddings(struct llama_context * ctx) {
+ llama_synchronize(ctx);
+
+ return ctx->embd;
+}
+
+float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i) {
+ int32_t j = -1;
+
+ llama_synchronize(ctx);
+
+ try {
+ if (ctx->embd == nullptr) {
+ throw std::runtime_error("no embeddings");
+ }
+
+ if (i < 0) {
+ j = ctx->n_outputs + i;
+ if (j < 0) {
+ throw std::runtime_error(format("negative index out of range [0, %d)", ctx->n_outputs));
+ }
+ } else if ((size_t) i >= ctx->output_ids.size()) {
+ throw std::runtime_error(format("out of range [0, %lu)", ctx->output_ids.size()));
+ } else {
+ j = ctx->output_ids[i];
+ }
+
+ if (j < 0) {
+ throw std::runtime_error(format("batch.logits[%d] != true", i));
+ }
+ if (j >= ctx->n_outputs) {
+ // This should not happen
+ throw std::runtime_error(format("corrupt output buffer (j=%d, n_outputs=%d)", j, ctx->n_outputs));
+ }
+
+ return ctx->embd + j*ctx->model.hparams.n_embd;
+ } catch (const std::exception & err) {
+ LLAMA_LOG_ERROR("%s: invalid embeddings id %d, reason: %s\n", __func__, i, err.what());
+#ifndef NDEBUG
+ GGML_ASSERT(false);
+#endif
+ return nullptr;
+ }
+}
+
+float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id) {
+ llama_synchronize(ctx);
+
+ auto it = ctx->embd_seq.find(seq_id);
+ if (it == ctx->embd_seq.end()) {
+ return nullptr;
+ }
+
+ return it->second.data();
+}
+
+//
+// vocab
+//
+
+const char * llama_token_get_text(const struct llama_model * model, llama_token token) {
+ return llama_token_get_text_impl(model->vocab, token);
+}
+
+float llama_token_get_score(const struct llama_model * model, llama_token token) {
+ return llama_token_get_score_impl(model->vocab, token);
+}
+
+enum llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token) {
+ return llama_token_get_attr_impl(model->vocab, token);
+}
+
+bool llama_token_is_eog(const struct llama_model * model, llama_token token) {
+ return llama_token_is_eog_impl(model->vocab, token);
+}
+
+bool llama_token_is_control(const struct llama_model * model, llama_token token) {
+ return llama_token_is_control_impl(model->vocab, token);
+}
+
+llama_token llama_token_bos(const struct llama_model * model) {
+ return llama_token_bos_impl(model->vocab);
+}
+
+llama_token llama_token_eos(const struct llama_model * model) {
+ return llama_token_eos_impl(model->vocab);
+}
+
+llama_token llama_token_cls(const struct llama_model * model) {
+ return llama_token_cls_impl(model->vocab);
+}
+
+llama_token llama_token_sep(const struct llama_model * model) {
+ return llama_token_sep_impl(model->vocab);
+}
+
+llama_token llama_token_nl (const struct llama_model * model) {
+ return llama_token_nl_impl(model->vocab);
+}
+
+llama_token llama_token_pad(const struct llama_model * model) {
+ return llama_token_pad_impl(model->vocab);
+}
+
+int32_t llama_add_bos_token(const struct llama_model * model) {
+ return llama_add_bos_token_impl(model->vocab);
+}
+
+int32_t llama_add_eos_token(const struct llama_model * model) {
+ return llama_add_eos_token_impl(model->vocab);
+}
+
+llama_token llama_token_prefix(const struct llama_model * model) {
+ return llama_token_prefix_impl(model->vocab);
+}
+
+llama_token llama_token_middle(const struct llama_model * model) {
+ return llama_token_middle_impl(model->vocab);
+}
+
+llama_token llama_token_suffix(const struct llama_model * model) {
+ return llama_token_suffix_impl(model->vocab);
+}
+
+llama_token llama_token_eot(const struct llama_model * model) {
+ return llama_token_eot_impl(model->vocab);
+}
+
+//
+// tokenization
+//
+
+int32_t llama_tokenize(
+ const struct llama_model * model,
+ const char * text,
+ int32_t text_len,
+ llama_token * tokens,
+ int32_t n_tokens_max,
+ bool add_special,
+ bool parse_special) {
+ return llama_tokenize_impl(model->vocab, text, text_len, tokens, n_tokens_max, add_special, parse_special);
+}
+
+int32_t llama_token_to_piece(
+ const struct llama_model * model,
+ llama_token token,
+ char * buf,
+ int32_t length,
+ int32_t lstrip,
+ bool special) {
+ return llama_token_to_piece_impl(model->vocab, token, buf, length, lstrip, special);
+}
+
+int32_t llama_detokenize(
+ const struct llama_model * model,
+ const llama_token * tokens,
+ int32_t n_tokens,
+ char * text,
+ int32_t text_len_max,
+ bool remove_special,
+ bool unparse_special) {
+ return llama_detokenize_impl(model->vocab, tokens, n_tokens, text, text_len_max, remove_special, unparse_special);
+}
+
+//
+// chat templates
+//
+
+// Simple version of "llama_apply_chat_template" that only works with strings
+// This function uses heuristic checks to determine commonly used template. It is not a jinja parser.
+static int32_t llama_chat_apply_template_internal(
+ const std::string & tmpl,
+ const std::vector<const llama_chat_message *> & chat,
+ std::string & dest, bool add_ass) {
+ // Taken from the research: https://github.com/ggerganov/llama.cpp/issues/5527
+ std::stringstream ss;
+ auto tmpl_contains = [&tmpl](std::string haystack) -> bool {
+ return tmpl.find(haystack) != std::string::npos;
+ };
+ if (tmpl == "chatml" || tmpl_contains("<|im_start|>")) {
+ // chatml template
+ for (auto message : chat) {
+ ss << "<|im_start|>" << message->role << "\n" << message->content << "<|im_end|>\n";
+ }
+ if (add_ass) {
+ ss << "<|im_start|>assistant\n";
+ }
+ } else if (tmpl == "llama2" || tmpl == "mistral" || tmpl_contains("[INST]")) {
+ // llama2 template and its variants
+ // [variant] support system message
+ bool support_system_message = tmpl_contains("<<SYS>>") || tmpl == "mistral";
+ // [variant] space before + after response
+ bool space_around_response = tmpl_contains("' ' + eos_token");
+ // [variant] add BOS inside history
+ bool add_bos_inside_history = tmpl_contains("bos_token + '[INST]");
+ // [variant] trim spaces from the input message
+ bool strip_message = tmpl_contains("content.strip()");
+ // construct the prompt
+ bool is_inside_turn = true; // skip BOS at the beginning
+ ss << "[INST] ";
+ for (auto message : chat) {
+ std::string content = strip_message ? trim(message->content) : message->content;
+ std::string role(message->role);
+ if (!is_inside_turn) {
+ is_inside_turn = true;
+ ss << (add_bos_inside_history ? "<s>[INST] " : "[INST] ");
+ }
+ if (role == "system") {
+ if (support_system_message) {
+ ss << "<<SYS>>\n" << content << "\n<</SYS>>\n\n";
+ } else {
+ // if the model does not support system message, we still include it in the first message, but without <<SYS>>
+ ss << content << "\n";
+ }
+ } else if (role == "user") {
+ ss << content << " [/INST]";
+ } else {
+ ss << (space_around_response ? " " : "") << content << (space_around_response ? " " : "") << "</s>";
+ is_inside_turn = false;
+ }
+ }
+ // llama2 templates seem to not care about "add_generation_prompt"
+ } else if (tmpl == "phi3" || (tmpl_contains("<|assistant|>") && tmpl_contains("<|end|>"))) {
+ // Phi 3
+ for (auto message : chat) {
+ std::string role(message->role);
+ ss << "<|" << role << "|>\n" << message->content << "<|end|>\n";
+ }
+ if (add_ass) {
+ ss << "<|assistant|>\n";
+ }
+ } else if (tmpl == "zephyr" || tmpl_contains("<|user|>")) {
+ // zephyr template
+ for (auto message : chat) {
+ ss << "<|" << message->role << "|>" << "\n" << message->content << "<|endoftext|>\n";
+ }
+ if (add_ass) {
+ ss << "<|assistant|>\n";
+ }
+ } else if (tmpl == "monarch" || tmpl_contains("bos_token + message['role']")) {
+ // mlabonne/AlphaMonarch-7B template (the <s> is included inside history)
+ for (auto message : chat) {
+ std::string bos = (message == chat.front()) ? "" : "<s>"; // skip BOS for first message
+ ss << bos << message->role << "\n" << message->content << "</s>\n";
+ }
+ if (add_ass) {
+ ss << "<s>assistant\n";
+ }
+ } else if (tmpl == "gemma" || tmpl == "gemma2" || tmpl_contains("<start_of_turn>")) {
+ // google/gemma-7b-it
+ std::string system_prompt = "";
+ for (auto message : chat) {
+ std::string role(message->role);
+ if (role == "system") {
+ // there is no system message for gemma, but we will merge it with user prompt, so nothing is broken
+ system_prompt = trim(message->content);
+ continue;
+ }
+ // in gemma, "assistant" is "model"
+ role = role == "assistant" ? "model" : message->role;
+ ss << "<start_of_turn>" << role << "\n";
+ if (!system_prompt.empty() && role != "model") {
+ ss << system_prompt << "\n\n";
+ system_prompt = "";
+ }
+ ss << trim(message->content) << "<end_of_turn>\n";
+ }
+ if (add_ass) {
+ ss << "<start_of_turn>model\n";
+ }
+ } else if (tmpl == "orion" || tmpl_contains("'\\n\\nAssistant: ' + eos_token")) {
+ // OrionStarAI/Orion-14B-Chat
+ std::string system_prompt = "";
+ for (auto message : chat) {
+ std::string role(message->role);
+ if (role == "system") {
+ // there is no system message support, we will merge it with user prompt
+ system_prompt = message->content;
+ continue;
+ } else if (role == "user") {
+ ss << "Human: ";
+ if (!system_prompt.empty()) {
+ ss << system_prompt << "\n\n";
+ system_prompt = "";
+ }
+ ss << message->content << "\n\nAssistant: </s>";
+ } else {
+ ss << message->content << "</s>";
+ }
+ }
+ } else if (tmpl == "openchat" || tmpl_contains("GPT4 Correct ")) {
+ // openchat/openchat-3.5-0106,
+ for (auto message : chat) {
+ std::string role(message->role);
+ if (role == "system") {
+ ss << message->content << "<|end_of_turn|>";
+ } else {
+ role[0] = toupper(role[0]);
+ ss << "GPT4 Correct " << role << ": " << message->content << "<|end_of_turn|>";
+ }
+ }
+ if (add_ass) {
+ ss << "GPT4 Correct Assistant:";
+ }
+ } else if (tmpl == "vicuna" || tmpl == "vicuna-orca" || (tmpl_contains("USER: ") && tmpl_contains("ASSISTANT: "))) {
+ // eachadea/vicuna-13b-1.1 (and Orca variant)
+ for (auto message : chat) {
+ std::string role(message->role);
+ if (role == "system") {
+ // Orca-Vicuna variant uses a system prefix
+ if (tmpl == "vicuna-orca" || tmpl_contains("SYSTEM: ")) {
+ ss << "SYSTEM: " << message->content << "\n";
+ } else {
+ ss << message->content << "\n\n";
+ }
+ } else if (role == "user") {
+ ss << "USER: " << message->content << "\n";
+ } else if (role == "assistant") {
+ ss << "ASSISTANT: " << message->content << "</s>\n";
+ }
+ }
+ if (add_ass) {
+ ss << "ASSISTANT:";
+ }
+ } else if (tmpl == "deepseek" || (tmpl_contains("### Instruction:") && tmpl_contains("<|EOT|>"))) {
+ // deepseek-ai/deepseek-coder-33b-instruct
+ for (auto message : chat) {
+ std::string role(message->role);
+ if (role == "system") {
+ ss << message->content;
+ } else if (role == "user") {
+ ss << "### Instruction:\n" << message->content << "\n";
+ } else if (role == "assistant") {
+ ss << "### Response:\n" << message->content << "\n<|EOT|>\n";
+ }
+ }
+ if (add_ass) {
+ ss << "### Response:\n";
+ }
+ } else if (tmpl == "command-r" || (tmpl_contains("<|START_OF_TURN_TOKEN|>") && tmpl_contains("<|USER_TOKEN|>"))) {
+ // CohereForAI/c4ai-command-r-plus
+ for (auto message : chat) {
+ std::string role(message->role);
+ if (role == "system") {
+ ss << "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
+ } else if (role == "user") {
+ ss << "<|START_OF_TURN_TOKEN|><|USER_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
+ } else if (role == "assistant") {
+ ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>" << trim(message->content) << "<|END_OF_TURN_TOKEN|>";
+ }
+ }
+ if (add_ass) {
+ ss << "<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>";
+ }
+ } else if (tmpl == "llama3" || (tmpl_contains("<|start_header_id|>") && tmpl_contains("<|end_header_id|>"))) {
+ // Llama 3
+ for (auto message : chat) {
+ std::string role(message->role);
+ ss << "<|start_header_id|>" << role << "<|end_header_id|>\n\n" << trim(message->content) << "<|eot_id|>";
+ }
+ if (add_ass) {
+ ss << "<|start_header_id|>assistant<|end_header_id|>\n\n";
+ }
+ } else if (tmpl == "chatglm3" || tmpl_contains("[gMASK]sop")) {
+ // chatglm3-6b
+ ss << "[gMASK]" << "sop";
+ for (auto message : chat) {
+ std::string role(message->role);
+ ss << "<|" << role << "|>" << "\n " << message->content;
+ }
+ if (add_ass) {
+ ss << "<|assistant|>";
+ }
+ } else if (tmpl == "chatglm4" || tmpl_contains("[gMASK]<sop>")) {
+ ss << "[gMASK]" << "<sop>";
+ for (auto message : chat) {
+ std::string role(message->role);
+ ss << "<|" << role << "|>" << "\n" << message->content;
+ }
+ if (add_ass) {
+ ss << "<|assistant|>";
+ }
+ } else if (tmpl == "minicpm" || tmpl_contains(LU8("<用户>"))) {
+ // MiniCPM-3B-OpenHermes-2.5-v2-GGUF
+ for (auto message : chat) {
+ std::string role(message->role);
+ if (role == "user") {
+ ss << LU8("<用户>");
+ ss << trim(message->content);
+ ss << "<AI>";
+ } else {
+ ss << trim(message->content);
+ }
+ }
+ } else if (tmpl == "deepseek2" || tmpl_contains("'Assistant: ' + message['content'] + eos_token")) {
+ // DeepSeek-V2
+ for (auto message : chat) {
+ std::string role(message->role);
+ if (role == "system") {
+ ss << message->content << "\n\n";
+ } else if (role == "user") {
+ ss << "User: " << message->content << "\n\n";
+ } else if (role == "assistant") {
+ ss << "Assistant: " << message->content << LU8("<｜end▁of▁sentence｜>");
+ }
+ }
+ if (add_ass) {
+ ss << "Assistant:";
+ }
+ } else {
+ // template not supported
+ return -1;
+ }
+ dest = ss.str();
+ return dest.size();
+}
+
+int32_t llama_chat_apply_template(
+ const struct llama_model * model,
+ const char * tmpl,
+ const struct llama_chat_message * chat,
+ size_t n_msg,
+ bool add_ass,
+ char * buf,
+ int32_t length) {
+ std::string curr_tmpl(tmpl == nullptr ? "" : tmpl);
+ if (tmpl == nullptr) {
+ GGML_ASSERT(model != nullptr);
+ // load template from model
+ std::vector<char> model_template(2048, 0); // longest known template is about 1200 bytes
+ std::string template_key = "tokenizer.chat_template";
+ int32_t res = llama_model_meta_val_str(model, template_key.c_str(), model_template.data(), model_template.size());
+ if (res < 0) {
+ // worst case: there is no information about template, we will use chatml by default
+ curr_tmpl = "chatml"; // see llama_chat_apply_template_internal
+ } else {
+ curr_tmpl = std::string(model_template.data(), model_template.size());
+ }
+ }
+
+ // format the chat to string
+ std::vector<const llama_chat_message *> chat_vec;
+ chat_vec.resize(n_msg);
+ for (size_t i = 0; i < n_msg; i++) {
+ chat_vec[i] = &chat[i];
+ }
+
+ std::string formatted_chat;
+ int32_t res = llama_chat_apply_template_internal(curr_tmpl, chat_vec, formatted_chat, add_ass);
+ if (res < 0) {
+ return res;
+ }
+ if (buf && length > 0) {
+ strncpy(buf, formatted_chat.c_str(), length);
+ }
+ return res;
+}
+
+//
+// grammar
+//
+
+struct llama_grammar * llama_grammar_init(
+ const llama_grammar_element ** rules,
+ size_t n_rules,
+ size_t start_rule_index) {
+ return llama_grammar_init_impl(rules, n_rules, start_rule_index);
+}
+
+void llama_grammar_free(struct llama_grammar * grammar) {
+ llama_grammar_free_impl(grammar);
+}
+
+struct llama_grammar * llama_grammar_copy(const struct llama_grammar * grammar) {
+ return llama_grammar_copy_impl(grammar);
+}
+
+void llama_grammar_sample(
+ const struct llama_grammar * grammar,
+ const struct llama_context * ctx,
+ llama_token_data_array * candidates) {
+ llama_grammar_sample_impl(grammar, &ctx->model.vocab, &ctx->sampling, candidates);
+}
+
+void llama_sample_grammar(
+ struct llama_context * ctx,
+ llama_token_data_array * candidates,
+ const struct llama_grammar * grammar) {
+ llama_grammar_sample(grammar, ctx, candidates);
+}
+
+void llama_grammar_accept_token(
+ struct llama_grammar * grammar,
+ struct llama_context * ctx,
+ llama_token token) {
+ llama_grammar_accept_token_impl(grammar, &ctx->model.vocab, &ctx->sampling, token);
+}
+
+//
+// sampling
+//
+
+void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed) {
+ llama_set_rng_seed_impl(&ctx->sampling, seed);
+}
+
+void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates) {
+ llama_sample_softmax_impl(ctx ? &ctx->sampling : nullptr, candidates);
+}
+
+void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int32_t k, size_t min_keep) {
+ llama_sample_top_k_impl(ctx ? &ctx->sampling : nullptr, candidates, k, min_keep);
+}
+
+void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
+ llama_sample_top_p_impl(ctx ? &ctx->sampling : nullptr, candidates, p, min_keep);
+}
+
+void llama_sample_min_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
+ llama_sample_min_p_impl(ctx ? &ctx->sampling : nullptr, candidates, p, min_keep);
+}
+
+void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep) {
+ llama_sample_tail_free_impl(ctx ? &ctx->sampling : nullptr, candidates, z, min_keep);
+}
+
+void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep) {
+ llama_sample_typical_impl(ctx ? &ctx->sampling : nullptr, candidates, p, min_keep);
+}
+
+void llama_sample_entropy(struct llama_context * ctx, llama_token_data_array * candidates_p, float min_temp, float max_temp, float exponent_val) {
+ llama_sample_entropy_impl(ctx ? &ctx->sampling : nullptr, candidates_p, min_temp, max_temp, exponent_val);
+}
+
+void llama_sample_temp(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) {
+ llama_sample_temp_impl(ctx ? &ctx->sampling : nullptr, candidates_p, temp);
+}
+
+void llama_sample_repetition_penalties(
+ struct llama_context * ctx,
+ llama_token_data_array * candidates,
+ const llama_token * last_tokens,
+ size_t penalty_last_n,
+ float penalty_repeat,
+ float penalty_freq,
+ float penalty_present) {
+ llama_sample_repetition_penalties_impl(ctx ? &ctx->sampling : nullptr, candidates, last_tokens, penalty_last_n, penalty_repeat, penalty_freq, penalty_present);
+}
+
+void llama_sample_apply_guidance(
+ struct llama_context * ctx,
+ float * logits,
+ float * logits_guidance,
+ float scale) {
+ llama_sample_apply_guidance_impl(&ctx->sampling, logits, logits_guidance, scale);
+}
+
+llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int32_t m, float * mu) {
+ return llama_sample_token_mirostat_impl(&ctx->sampling, candidates, tau, eta, m, mu);
+}
+
+llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) {
+ return llama_sample_token_mirostat_v2_impl(ctx ? &ctx->sampling : nullptr, candidates, tau, eta, mu);
+}
+
+llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates) {
+ return llama_sample_token_greedy_impl(ctx ? &ctx->sampling : nullptr, candidates);
+}
+
+llama_token llama_sample_token_with_rng(struct llama_context * ctx, llama_token_data_array * candidates, std::mt19937 & rng) {
+ return llama_sample_token_with_rng_impl(&ctx->sampling, candidates, rng);
+}
+
+llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates) {
+ return llama_sample_token_with_rng_impl(&ctx->sampling, candidates, ctx->sampling.rng);
+}
+
+int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count) {
+ static const char * const SPLIT_PATH_FORMAT = "%s-%05d-of-%05d.gguf";
+ if (snprintf(split_path, maxlen, SPLIT_PATH_FORMAT, path_prefix, split_no + 1, split_count)) {
+ return strlen(split_path);
+ }
+ return 0;
+}
+
+int llama_split_prefix(char * dest, size_t maxlen, const char * split_path, int split_no, int split_count) {
+ std::string str_split_path(split_path);
+ char postfix[32];
+ snprintf(postfix, 32, "-%05d-of-%05d.gguf", split_no + 1, split_count);
+ std::string str_postfix(postfix);
+
+ // check if dest ends with postfix
+ int size_prefix = str_split_path.size() - str_postfix.size();
+ if (size_prefix > 0 && str_split_path.find(str_postfix, size_prefix) != std::string::npos) {
+ snprintf(dest, std::min((size_t) size_prefix + 1, maxlen), "%s", split_path);
+ return size_prefix;
+ }
+
+ return 0;
+}
+
+struct llama_timings llama_get_timings(struct llama_context * ctx) {
+ struct llama_timings result = {
+ /*.t_start_ms =*/ 1e-3 * ctx->t_start_us,
+ /*.t_end_ms =*/ 1.00 * ggml_time_ms(),
+ /*.t_load_ms =*/ 1e-3 * ctx->t_load_us,
+ /*.t_sample_ms =*/ 1e-3 * ctx->sampling.t_sample_us,
+ /*.t_p_eval_ms =*/ 1e-3 * ctx->t_p_eval_us,
+ /*.t_eval_ms =*/ 1e-3 * ctx->t_eval_us,
+
+ /*.n_sample =*/ std::max(1, ctx->sampling.n_sample),
+ /*.n_p_eval =*/ std::max(0, ctx->n_p_eval),
+ /*.n_eval =*/ std::max(1, ctx->n_eval),
+ };
+
+ return result;
+}
+
+void llama_print_timings(struct llama_context * ctx) {
+ const llama_timings timings = llama_get_timings(ctx);
+
+ LLAMA_LOG_INFO("\n");
+ LLAMA_LOG_INFO("%s: load time = %10.2f ms\n", __func__, timings.t_load_ms);
+ LLAMA_LOG_INFO("%s: sample time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
+ __func__, timings.t_sample_ms, timings.n_sample, timings.t_sample_ms / timings.n_sample, 1e3 / timings.t_sample_ms * timings.n_sample);
+ LLAMA_LOG_INFO("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
+ __func__, timings.t_p_eval_ms, timings.n_p_eval, timings.t_p_eval_ms / timings.n_p_eval, 1e3 / timings.t_p_eval_ms * timings.n_p_eval);
+ LLAMA_LOG_INFO("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
+ __func__, timings.t_eval_ms, timings.n_eval, timings.t_eval_ms / timings.n_eval, 1e3 / timings.t_eval_ms * timings.n_eval);
+ LLAMA_LOG_INFO("%s: total time = %10.2f ms / %5d tokens\n", __func__, (timings.t_end_ms - timings.t_start_ms), (timings.n_p_eval + timings.n_eval));
+}
+
+void llama_reset_timings(struct llama_context * ctx) {
+ ctx->t_start_us = ggml_time_us();
+ ctx->t_eval_us = ctx->n_eval = 0;
+ ctx->t_p_eval_us = ctx->n_p_eval = 0;
+
+ ctx->sampling.reset_timings();
+}
+
+const char * llama_print_system_info(void) {
+ static std::string s;
+
+ s = "";
+ s += "AVX = " + std::to_string(ggml_cpu_has_avx()) + " | ";
+ s += "AVX_VNNI = " + std::to_string(ggml_cpu_has_avx_vnni()) + " | ";
+ s += "AVX2 = " + std::to_string(ggml_cpu_has_avx2()) + " | ";
+ s += "AVX512 = " + std::to_string(ggml_cpu_has_avx512()) + " | ";
+ s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | ";
+ s += "AVX512_VNNI = " + std::to_string(ggml_cpu_has_avx512_vnni()) + " | ";
+ s += "AVX512_BF16 = " + std::to_string(ggml_cpu_has_avx512_bf16()) + " | ";
+ s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " | ";
+ s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " | ";
+ s += "SVE = " + std::to_string(ggml_cpu_has_sve()) + " | ";
+ s += "ARM_FMA = " + std::to_string(ggml_cpu_has_arm_fma()) + " | ";
+ s += "F16C = " + std::to_string(ggml_cpu_has_f16c()) + " | ";
+ s += "FP16_VA = " + std::to_string(ggml_cpu_has_fp16_va()) + " | ";
+ s += "WASM_SIMD = " + std::to_string(ggml_cpu_has_wasm_simd()) + " | ";
+ s += "BLAS = " + std::to_string(ggml_cpu_has_blas()) + " | ";
+ s += "SSE3 = " + std::to_string(ggml_cpu_has_sse3()) + " | ";
+ s += "SSSE3 = " + std::to_string(ggml_cpu_has_ssse3()) + " | ";
+ s += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | ";
+ s += "MATMUL_INT8 = " + std::to_string(ggml_cpu_has_matmul_int8()) + " | ";
+ s += "LLAMAFILE = " + std::to_string(ggml_cpu_has_llamafile()) + " | ";
+
+ return s.c_str();
+}
+
+void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx) {
+ fprintf(stream, "\n");
+ fprintf(stream, "###########\n");
+ fprintf(stream, "# Timings #\n");
+ fprintf(stream, "###########\n");
+ fprintf(stream, "\n");
+
+ fprintf(stream, "mst_eval: %.2f # ms / token during generation\n",
+ 1.0e-3 * ctx->t_eval_us / ctx->n_eval);
+ fprintf(stream, "mst_p_eval: %.2f # ms / token during prompt processing\n",
+ 1.0e-3 * ctx->t_p_eval_us / ctx->n_p_eval);
+ fprintf(stream, "mst_sample: %.2f # ms / token during sampling\n",
+ 1.0e-3 * ctx->sampling.t_sample_us / ctx->sampling.n_sample);
+ fprintf(stream, "n_eval: %d # number of tokens generated (excluding the first one)\n", ctx->n_eval);
+ fprintf(stream, "n_p_eval: %d # number of tokens processed in batches at the beginning\n", ctx->n_p_eval);
+ fprintf(stream, "n_sample: %d # number of sampled tokens\n", ctx->sampling.n_sample);
+ fprintf(stream, "t_eval_us: %" PRId64 " # total microseconds spent generating tokens\n", ctx->t_eval_us);
+ fprintf(stream, "t_load_us: %" PRId64 " # total microseconds spent loading the model\n", ctx->t_load_us);
+ fprintf(stream, "t_p_eval_us: %" PRId64 " # total microseconds spent prompt processing\n", ctx->t_p_eval_us);
+ fprintf(stream, "t_sample_us: %" PRId64 " # total microseconds spent sampling\n", ctx->sampling.t_sample_us);
+ fprintf(stream, "ts_eval: %.2f # tokens / second during generation\n",
+ 1.0e6 * ctx->n_eval / ctx->t_eval_us);
+ fprintf(stream, "ts_p_eval: %.2f # tokens / second during prompt processing\n",
+ 1.0e6 * ctx->n_p_eval / ctx->t_p_eval_us);
+ fprintf(stream, "ts_sample: %.2f # tokens / second during sampling\n",
+ 1.0e6 * ctx->sampling.n_sample / ctx->sampling.t_sample_us);
+}
+
+// For internal test use
+const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
+ struct llama_context * ctx
+) {
+ return ctx->model.tensors_by_name;
+}
+
+void llama_log_set(ggml_log_callback log_callback, void * user_data) {
+ g_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
+ g_state.log_callback_user_data = user_data;
+#ifdef GGML_USE_METAL
+ ggml_backend_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
+#elif defined(GGML_USE_CUDA)
+ ggml_backend_cuda_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
+#elif defined(GGML_USE_CANN)
+ ggml_backend_cann_log_set_callback(g_state.log_callback, g_state.log_callback_user_data);
+#endif
+}
+
+static void llama_log_internal_v(ggml_log_level level, const char * format, va_list args) {
+ va_list args_copy;
+ va_copy(args_copy, args);
+ char buffer[128];
+ int len = vsnprintf(buffer, 128, format, args);
+ if (len < 128) {
+ g_state.log_callback(level, buffer, g_state.log_callback_user_data);
+ } else {
+ char* buffer2 = new char[len+1];
+ vsnprintf(buffer2, len+1, format, args_copy);
+ buffer2[len] = 0;
+ g_state.log_callback(level, buffer2, g_state.log_callback_user_data);
+ delete[] buffer2;
+ }
+ va_end(args_copy);
+}
+
+void llama_log_internal(ggml_log_level level, const char * format, ...) {
+ va_list args;
+ va_start(args, format);
+ llama_log_internal_v(level, format, args);
+ va_end(args);
+}
+
+void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
+ (void) level;
+ (void) user_data;
+ fputs(text, stderr);
+ fflush(stderr);
+}
generated by cgit v1.2.3 (git 2.25.1) at 2025-08-15 14:24:24 +0000