#include "llama-impl.h"
#include <map>
-#include <set>
#include <vector>
static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_RND1, "rnd1" },
{ LLM_ARCH_PANGU_EMBED, "pangu-embedded" },
{ LLM_ARCH_MISTRAL3, "mistral3" },
+ { LLM_ARCH_EAGLE3, "eagle3" },
{ LLM_ARCH_MISTRAL4, "mistral4" },
{ LLM_ARCH_PADDLEOCR, "paddleocr" },
{ LLM_ARCH_MIMO2, "mimo2" },
{ LLM_KV_CLASSIFIER_OUTPUT_LABELS, "%s.classifier.output_labels" },
+ { LLM_KV_TARGET_LAYERS, "%s.target_layers" },
+ { LLM_KV_TARGET_HIDDEN_SIZE, "%s.target_hidden_size" },
+ { LLM_KV_NORM_BEFORE_RESIDUAL, "%s.norm_before_residual" },
+
{ LLM_KV_SHORTCONV_L_CACHE, "%s.shortconv.l_cache" },
// sentence-transformers dense modules feature dims
{ LLM_KV_DENSE_2_FEAT_IN, "%s.dense_2_feat_in" },
- { LLM_KV_DENSE_2_FEAT_OUT, "%s.dense_2_feat_out" },
- { LLM_KV_DENSE_3_FEAT_IN, "%s.dense_3_feat_in" },
- { LLM_KV_DENSE_3_FEAT_OUT, "%s.dense_3_feat_out" },
-
- { 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_EOT_ID, "tokenizer.ggml.eot_token_id" },
- { LLM_KV_TOKENIZER_EOM_ID, "tokenizer.ggml.eom_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_SEP, "tokenizer.ggml.add_sep_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_CHAT_TEMPLATE, "tokenizer.chat_template" },
- { LLM_KV_TOKENIZER_NORMALIZER_LOWERCASE, "tokenizer.ggml.normalizer.lowercase" },
- { LLM_KV_TOKENIZER_FIM_PRE_ID, "tokenizer.ggml.fim_pre_token_id" },
- { LLM_KV_TOKENIZER_FIM_SUF_ID, "tokenizer.ggml.fim_suf_token_id" },
- { LLM_KV_TOKENIZER_FIM_MID_ID, "tokenizer.ggml.fim_mid_token_id" },
- { LLM_KV_TOKENIZER_FIM_PAD_ID, "tokenizer.ggml.fim_pad_token_id" },
- { LLM_KV_TOKENIZER_FIM_REP_ID, "tokenizer.ggml.fim_rep_token_id" },
- { LLM_KV_TOKENIZER_FIM_SEP_ID, "tokenizer.ggml.fim_sep_token_id" },
- { LLM_KV_TOKENIZER_SUPPRESS_TOKENS, "tokenizer.ggml.suppress_tokens" },
+ { LLM_KV_DENSE_2_FEAT_OUT, "%s.dense_2_feat_out" },
+ { LLM_KV_DENSE_3_FEAT_IN, "%s.dense_3_feat_in" },
+ { LLM_KV_DENSE_3_FEAT_OUT, "%s.dense_3_feat_out" },
+
+ { 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_EOT_ID, "tokenizer.ggml.eot_token_id" },
+ { LLM_KV_TOKENIZER_EOM_ID, "tokenizer.ggml.eom_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_SEP, "tokenizer.ggml.add_sep_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_CHAT_TEMPLATE, "tokenizer.chat_template" },
+ { LLM_KV_TOKENIZER_NORMALIZER_LOWERCASE, "tokenizer.ggml.normalizer.lowercase" },
+ { LLM_KV_TOKENIZER_NORMALIZER_STRIP_ACCENTS, "tokenizer.ggml.normalizer.strip_accents" },
+ { LLM_KV_TOKENIZER_FIM_PRE_ID, "tokenizer.ggml.fim_pre_token_id" },
+ { LLM_KV_TOKENIZER_FIM_SUF_ID, "tokenizer.ggml.fim_suf_token_id" },
+ { LLM_KV_TOKENIZER_FIM_MID_ID, "tokenizer.ggml.fim_mid_token_id" },
+ { LLM_KV_TOKENIZER_FIM_PAD_ID, "tokenizer.ggml.fim_pad_token_id" },
+ { LLM_KV_TOKENIZER_FIM_REP_ID, "tokenizer.ggml.fim_rep_token_id" },
+ { LLM_KV_TOKENIZER_FIM_SEP_ID, "tokenizer.ggml.fim_sep_token_id" },
+ { LLM_KV_TOKENIZER_SUPPRESS_TOKENS, "tokenizer.ggml.suppress_tokens" },
{ LLM_KV_ADAPTER_TYPE, "adapter.type" },
{ LLM_KV_ADAPTER_LORA_ALPHA, "adapter.lora.alpha" },
{ LLM_TENSOR_INDEXER_PROJ, "blk.%d.indexer.proj" },
{ LLM_TENSOR_INDEXER_ATTN_K, "blk.%d.indexer.attn_k" },
{ LLM_TENSOR_INDEXER_ATTN_Q_B, "blk.%d.indexer.attn_q_b" },
+ { LLM_TENSOR_MASKED_EMBD_CENTROIDS, "masked_embd_centroids" },
+ { LLM_TENSOR_MASKED_EMBD_ORDERING, "masked_embd_ordering" },
+ { LLM_TENSOR_FC, "fc" },
+ { LLM_TENSOR_D2T, "d2t" },
};
// declare information about the model weight tensors:
// latent projections feed ggml_mul_mat, the buft probe must use MUL_MAT to keep them on GPU
{LLM_TENSOR_FFN_LATENT_DOWN, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_FFN_LATENT_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_MASKED_EMBD_CENTROIDS, {LLM_TENSOR_LAYER_INPUT, GGML_OP_NONE}},
+ {LLM_TENSOR_MASKED_EMBD_ORDERING, {LLM_TENSOR_LAYER_INPUT, GGML_OP_NONE}},
+ // eagle3
+ {LLM_TENSOR_FC, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_D2T, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}},
};
LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {}
LLM_ARCH_KIMI_LINEAR,
LLM_ARCH_TALKIE,
LLM_ARCH_MELLUM,
+ LLM_ARCH_EAGLE3,
LLM_ARCH_UNKNOWN,
};
LLM_KV_TOKENIZER_RWKV,
LLM_KV_TOKENIZER_CHAT_TEMPLATE,
LLM_KV_TOKENIZER_NORMALIZER_LOWERCASE,
+ LLM_KV_TOKENIZER_NORMALIZER_STRIP_ACCENTS,
LLM_KV_TOKENIZER_FIM_PRE_ID,
LLM_KV_TOKENIZER_FIM_SUF_ID,
LLM_KV_TOKENIZER_FIM_MID_ID,
LLM_KV_CLASSIFIER_OUTPUT_LABELS,
+ LLM_KV_TARGET_LAYERS,
+ LLM_KV_TARGET_HIDDEN_SIZE,
+ LLM_KV_NORM_BEFORE_RESIDUAL,
+
LLM_KV_SHORTCONV_L_CACHE,
LLM_KV_XIELU_ALPHA_N,
LLM_TENSOR_NEXTN_HNORM,
LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,
LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,
+ LLM_TENSOR_MASKED_EMBD_CENTROIDS,
+ LLM_TENSOR_MASKED_EMBD_ORDERING,
+ LLM_TENSOR_FC,
+ LLM_TENSOR_D2T,
};
+
enum llm_tensor_layer {
LLM_TENSOR_LAYER_INPUT,
LLM_TENSOR_LAYER_REPEATING,
cparams.no_perf = params.no_perf;
cparams.warmup = false;
+ cparams.embeddings_layer_inp.resize(hparams.n_layer(), false);
+ embd_layer_inp.resize(hparams.n_layer());
+
cparams.ctx_type = params.ctx_type;
cparams.pooling_type = params.pooling_type;
if (model.arch == LLM_ARCH_GEMMA4_ASSISTANT) {
if (params.ctx_other == nullptr) {
// TODO: change from runtime_error to llama_exception to avoid printing error message
- throw std::runtime_error("Gemma4Assistant requires ctx_other to be set (this is normal during memory fitting)");
+ throw std::runtime_error("Gemma4Assistant requires ctx_other to be set (this warning is normal during memory fitting)");
}
cparams.ctx_other = params.ctx_other;
}
+ if (model.arch == LLM_ARCH_EAGLE3) {
+ if (model.tok_embd == nullptr || model.output == nullptr) {
+ if (params.ctx_other == nullptr) {
+ throw std::runtime_error("EAGLE3 requires ctx_other to be set (this warning is normal during memory fitting)");
+ }
+ cparams.ctx_other = params.ctx_other;
+ }
+ }
+
// Initialize backend samplers here so they are part of the sampling graph
// before the reserve passes run later in this function. This avoids a later
// re-reserve when graph nodes change.
cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
- cparams.n_outputs_max = params.n_outputs_max == 0 ? cparams.n_batch : params.n_outputs_max;
+ cparams.n_outputs_max = params.n_outputs_max == 0 || llama_model_has_encoder(&model) ? cparams.n_batch : params.n_outputs_max;
cparams.op_offload = params.op_offload;
cparams.kv_unified = params.kv_unified;
}
}
+float * llama_context::get_embeddings_layer_inp(uint32_t lid) {
+ output_reorder();
+
+ GGML_ASSERT(lid < embd_layer_inp.size() && embd_layer_inp[lid].has_data());
+
+ return embd_layer_inp[lid].data;
+}
+
llama_token llama_context::get_sampled_token_ith(int32_t idx) {
output_reorder();
cparams.embeddings_nextn_masked = masked;
}
+void llama_context::set_embeddings_layer_inp(uint32_t lid, bool enable) {
+ LLAMA_LOG_DEBUG("%s: lid = %d, enable = %d\n", __func__, lid, enable);
+
+ GGML_ASSERT(lid < model.hparams.n_layer());
+
+ cparams.embeddings_layer_inp[lid] = enable;
+
+ // note: without this reserve, the draft acceptance drops to zero. not sure why - this is unexpected
+ sched_need_reserve = true;
+}
+
void llama_context::set_causal_attn(bool value) {
LLAMA_LOG_DEBUG("%s: value = %d\n", __func__, value);
const auto & hparams = model.hparams;
- const int64_t n_embd = hparams.n_embd_inp();
+ // eagle3/DFlash: features as encoder input, and non-draft paths fall back to model's input dim
+ const int64_t n_embd = hparams.n_embd_inp();
const int64_t n_vocab = model.vocab.n_tokens();
// note: during encode, we always pass the full sequence starting from pos = 0
}
}
+ extract_layer_inputs(res, n_tokens_prev, ubatch.n_tokens);
+
// extract nextn embeddings before
// only meaningful in LLAMA_POOLING_TYPE_NONE (per-token); other pooling modes are ignored.
{
const auto n_batch = cparams.n_batch;
const auto n_vocab = vocab.n_tokens();
+ const auto n_embd = hparams.n_embd;
const auto n_embd_out = hparams.n_embd_out();
bool has_logits = true;
has_embd = true;
}
-
size_t backend_float_count = 0;
size_t backend_token_count = 0;
+ size_t embd_layer_inp_float_count = 0;
logits.size = has_logits ? n_vocab*n_outputs_max : 0;
embd.size = has_embd ? n_embd_out*n_outputs_max : 0;
embd_nextn.size = (size_t) n_embd_out * n_batch;
}
+ for (bool enabled : cparams.embeddings_layer_inp) {
+ if (enabled) {
+ embd_layer_inp_float_count += (size_t) n_embd * n_batch;
+ }
+ }
+
// Allocate backend sampling output buffers if there are backend samplers configured.
const bool has_sampling = !sampling.samplers.empty();
if (has_sampling) {
const size_t prev_size = buf_output ? ggml_backend_buffer_get_size(buf_output.get()) : 0;
const size_t new_size =
- (logits.size + embd.size + embd_nextn.size + backend_float_count) * sizeof(float) +
- ( backend_token_count) * sizeof(llama_token);
+ (logits.size + embd.size + embd_nextn.size + embd_layer_inp_float_count + backend_float_count) * sizeof(float) +
+ ( backend_token_count) * sizeof(llama_token);
// alloc only when more than the current capacity is required
// TODO: also consider shrinking the buffer
logits.data = nullptr;
embd.data = nullptr;
embd_nextn.data = nullptr;
+ for (auto & layer_inp : embd_layer_inp) {
+ layer_inp = {nullptr, 0};
+ }
}
auto * buft = ggml_backend_cpu_buffer_type();
embd_nextn = has_embd_nextn ? buffer_view<float>{(float *) (base + offset), embd_nextn.size} : buffer_view<float>{nullptr, 0};
offset += embd_nextn.size * sizeof(float);
+ for (uint32_t il = 0; il < embd_layer_inp.size(); ++il) {
+ if (cparams.embeddings_layer_inp[il]) {
+ embd_layer_inp[il] = buffer_view<float>{(float *) (base + offset), (size_t) n_embd * n_batch};
+ offset += embd_layer_inp[il].size * sizeof(float);
+ } else {
+ embd_layer_inp[il] = buffer_view<float>{nullptr, 0};
+ }
+ }
+
if (has_sampling) {
sampling.logits = {(float *) (base + offset), (size_t)(n_vocab*n_outputs_max)};
offset += sampling.logits.size * sizeof(float);
return n_outputs_max;
}
+void llama_context::extract_layer_inputs(const llm_graph_result * res, size_t token_offset, size_t n_tokens) {
+ for (uint32_t il = 0; il < cparams.embeddings_layer_inp.size(); ++il) {
+ if (!cparams.embeddings_layer_inp[il]) {
+ continue;
+ }
+ if (!embd_layer_inp[il].has_data()) {
+ GGML_ABORT("output layer input buffer not allocated");
+ }
+ ggml_tensor * t = res->get_layer_inp((int) il);
+ if (!t) {
+ GGML_ABORT("layer input tensor not found");
+ }
+
+ const size_t nbytes = ggml_nbytes(t);
+ const size_t nfloats = nbytes / sizeof(float);
+ GGML_ASSERT(n_tokens > 0);
+ GGML_ASSERT(nfloats % n_tokens == 0);
+
+ const size_t row_floats = nfloats / n_tokens;
+ const size_t dst_offset = token_offset * row_floats;
+ GGML_ASSERT(dst_offset + nfloats <= embd_layer_inp[il].size);
+
+ ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched.get(), t);
+ GGML_ASSERT(backend != nullptr);
+ ggml_backend_tensor_get_async(backend, t, embd_layer_inp[il].data + dst_offset, 0, nbytes);
+ }
+}
+
void llama_context::output_reorder() {
const uint64_t n_vocab = model.vocab.n_tokens();
const uint64_t n_embd = model.hparams.n_embd;
}
}
+ if (embd_layer_inp.size() > 0) {
+ for (int lid = 0; lid < (int) embd_layer_inp.size(); ++lid) {
+ if (embd_layer_inp[lid].size > 0) {
+ for (uint64_t k = 0; k < n_embd; ++k) {
+ std::swap(embd_layer_inp[lid].data[i0*n_embd + k], embd_layer_inp[lid].data[i1*n_embd + k]);
+ }
+ }
+ }
+ }
+
if (!sampling.samplers.empty()) {
assert(sampling.logits.size > 0);
assert(sampling.probs.size > 0);
ctx->set_embeddings_nextn(value, masked);
}
+void llama_set_embeddings_layer_inp(llama_context * ctx, uint32_t lid, bool value) {
+ ctx->set_embeddings_layer_inp(lid, value);
+}
+
llama_memory_t llama_get_memory(const struct llama_context * ctx) {
if (!ctx) {
return nullptr;
return ctx->get_embeddings_nextn_ith(i);
}
+float * llama_get_embeddings_layer_inp(llama_context * ctx, uint32_t lid) {
+ ctx->synchronize();
+
+ return ctx->get_embeddings_layer_inp(lid);
+}
+
bool llama_set_sampler(llama_context * ctx, llama_seq_id seq_id, llama_sampler * smpl) {
return ctx->set_sampler(seq_id, smpl);
}
float * get_embeddings_nextn();
float * get_embeddings_nextn_ith(int32_t i);
+ float * get_embeddings_layer_inp(uint32_t lid);
+
llama_token * get_sampled_tokens() const;
llama_token get_sampled_token_ith(int32_t idx);
void set_embeddings (bool value);
void set_embeddings_nextn(bool value, bool masked);
+ void set_embeddings_layer_inp(uint32_t lid, bool enable);
void set_causal_attn(bool value);
void set_warmup(bool value);
// map the output row index `i` to batch index
int64_t output_resolve_row(int32_t i) const;
+ // async-copy enabled layer-input tensors (per cparams.output_layer_inp)
+ // from backend into host-side embd_layer_inp buffers
+ void extract_layer_inputs(const llm_graph_result * res, size_t token_offset, size_t n_tokens);
+
//
// graph
//
// sets llm_graph_result::t_h_nextn
buffer_view<float> embd_nextn = {nullptr, 0};
+ // host buffers for output layer input embeddings, per layer
+ // populated when cparams.output_layer_inp[il] is true
+ std::vector<buffer_view<float>> embd_layer_inp;
+
struct sampling_info {
// !samplers.empty() to check if any samplers are active
std::map<llama_seq_id, llama_sampler *> samplers;
#include "llama.h"
#include <cstdint>
+#include <vector>
#define LLAMA_MAX_SEQ 256
bool kv_unified;
bool pipeline_parallel;
+ std::vector<bool> embeddings_layer_inp; // [n_layer()] extract input embeddings for layer
+
enum llama_context_type ctx_type;
enum llama_pooling_type pooling_type;
// LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i);
LLAMA_API float * llama_get_embeddings_nextn_ith(struct llama_context * ctx, int32_t i);
+// Set whether the context outputs the input embeddings of a specific layer
+LLAMA_API void llama_set_embeddings_layer_inp(struct llama_context * ctx, uint32_t lid, bool value);
+
+// mirrors:
+// LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
+LLAMA_API float * llama_get_embeddings_layer_inp(struct llama_context * ctx, uint32_t lid);
+
LLAMA_API llama_context * llama_get_ctx_other(struct llama_context * ctx);
+
+//
+// model/context data extraction
+//
+
+// returns pointer to the target-model layer indices
+LLAMA_API const int32_t * llama_model_target_layer_ids (const struct llama_model * model);
+// returns the number of extracted layers from target model
+LLAMA_API uint32_t llama_model_target_layer_ids_n(const struct llama_model * model);
mctx->get_base()->set_input_v_idxs(self_v_idxs, ubatch);
}
- mctx->get_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
+ // the kq mask guards on its own buffer: shared cells leave idxs unbacked while the mask stays live
+ if (self_kq_mask && self_kq_mask->buffer) {
+ mctx->get_base()->set_input_kq_mask(self_kq_mask, ubatch, cparams.causal_attn);
+ }
// swa tensors may not be allocated if there are no SWA attention layers
if (self_k_idxs_swa && self_k_idxs_swa->buffer) {
mctx->get_swa()->set_input_v_idxs(self_v_idxs_swa, ubatch);
}
- mctx->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
+ if (self_kq_mask_swa && self_kq_mask_swa->buffer) {
+ mctx->get_swa()->set_input_kq_mask(self_kq_mask_swa, ubatch, cparams.causal_attn);
+ }
if (self_k_rot) {
mctx->get_base()->set_input_k_rot(self_k_rot);
//res &= self_v_idxs->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
}
- res &= can_reuse_kq_mask(self_kq_mask, mctx->get_base(), params.ubatch, params.cparams);
+ if (self_kq_mask && self_kq_mask->buffer) {
+ res &= can_reuse_kq_mask(self_kq_mask, mctx->get_base(), params.ubatch, params.cparams);
+ }
// swa tensors may not be allocated if there are no SWA attention layers
if (self_k_idxs_swa && self_k_idxs_swa->buffer) {
//res &= self_v_idxs_swa->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
}
- res &= can_reuse_kq_mask(self_kq_mask_swa, mctx->get_swa(), params.ubatch, params.cparams);
+ if (self_kq_mask_swa && self_kq_mask_swa->buffer) {
+ res &= can_reuse_kq_mask(self_kq_mask_swa, mctx->get_swa(), params.ubatch, params.cparams);
+ }
return res;
}
t_logits = nullptr;
t_embd = nullptr;
t_embd_pooled = nullptr;
+
+ t_layer_inp.resize(LLAMA_MAX_LAYERS);
+ std::fill(t_layer_inp.begin(), t_layer_inp.end(), nullptr);
+
t_sampled.clear();
t_sampled_probs.clear();
t_sampled_logits.clear();
}
}
-void llm_graph_result::set_outputs() {
+void llm_graph_result::set_outputs(const llm_graph_params & params) {
if (t_logits != nullptr) {
ggml_set_output(t_logits);
}
if (t_h_nextn != nullptr) {
ggml_set_output(t_h_nextn);
}
+ {
+ const auto & embeddings_layer_inp = params.cparams.embeddings_layer_inp;
+ for (size_t il = 0; il < embeddings_layer_inp.size(); ++il) {
+ if (embeddings_layer_inp[il]) {
+ GGML_ASSERT(t_layer_inp[il] != nullptr && "layer input tensor is null");
+ ggml_set_output(t_layer_inp[il]);
+ }
+ }
+ }
for (auto & [seq_id, t] : t_sampled) {
if (t != nullptr) {
ggml_set_output(t);
res->t_inp_embd = cur;
// For Granite architecture
- // NOTE: Only apply scale to token inputs. Raw embeddings are assumed to be
- // multimodal inputs that should not be scaled.
- if (ubatch.token && hparams.f_embedding_scale != 0.0f) {
+ // NOTE: For deepstack models, only apply scale to token inputs (ie text-only input).
+ // Raw embeddings are assumed to be multimodal inputs that should not be scaled.
+ if (hparams.f_embedding_scale != 0.0f && (ubatch.token || hparams.n_deepstack_layers == 0)) {
if (!ggml_is_contiguous(cur)) {
cur = ggml_cont(ctx0, cur);
}
ggml_tensor * get_embd_pooled() const { return t_embd_pooled; }
ggml_tensor * get_h_nextn() const { return t_h_nextn; }
+ ggml_tensor * get_layer_inp(int il) const { return t_layer_inp[il]; }
+
ggml_cgraph * get_gf() const { return gf; }
ggml_context * get_ctx() const { return ctx_compute.get(); }
void reset();
void set_inputs(const llama_ubatch * ubatch);
- void set_outputs();
+ void set_outputs(const llm_graph_params & params);
// try to update the existing graph result using the new graph parameters in order to reuse it
// this can only be done if we determine that the resulting graph using the new graph parameters
ggml_tensor * t_embd_pooled = nullptr;
ggml_tensor * t_h_nextn = nullptr; // [n_embd, n_outputs] hidden state before final output norm
- std::map<llama_seq_id, ggml_tensor*> t_sampled_logits;
- std::map<llama_seq_id, ggml_tensor*> t_candidates;
- std::map<llama_seq_id, ggml_tensor*> t_sampled;
- std::map<llama_seq_id, ggml_tensor*> t_sampled_probs;
+ std::vector<ggml_tensor *> t_layer_inp;
+
+ std::map<llama_seq_id, ggml_tensor *> t_sampled_logits;
+ std::map<llama_seq_id, ggml_tensor *> t_candidates;
+ std::map<llama_seq_id, ggml_tensor *> t_sampled;
+ std::map<llama_seq_id, ggml_tensor *> t_sampled_probs;
std::vector<llm_graph_input_ptr> inputs;
bool rope_finetuned;
bool use_par_res;
bool swin_norm;
+ bool norm_before_residual = false;
uint32_t n_ctx_train; // context size the model was trained on
uint32_t n_embd;
template bool llama_model_loader::get_arr<std::vector<std::string>>(enum llm_kv kid, std::vector<std::string> & result, bool required);
template bool llama_model_loader::get_arr<std::array<int32_t, 512>>(enum llm_kv kid, std::array<int32_t, 512> & result, bool required);
+ template bool llama_model_loader::get_arr<std::vector<int32_t>>(enum llm_kv kid, std::vector<int32_t> & result, bool required);
template<typename T>
bool llama_model_loader::get_key(const std::string & key, T & result, bool required) {
return new llama_model_qwen35moe(params);
case LLM_ARCH_MISTRAL3:
return new llama_model_mistral3(params);
+ case LLM_ARCH_EAGLE3:
+ return new llama_model_eagle3(params);
case LLM_ARCH_MIMO2:
return new llama_model_mimo2(params);
case LLM_ARCH_KIMI_LINEAR:
// TODO: move reranking logic here and generalize
llm->build_dense_out(dense_2_out_layers, dense_2_out_layers_b, dense_3_out_layers);
- llm->res->set_outputs();
+ llm->res->set_outputs(params);
return llm->res->get_gf();
}
case LLM_ARCH_ERNIE4_5:
case LLM_ARCH_ERNIE4_5_MOE:
case LLM_ARCH_MISTRAL3:
+ case LLM_ARCH_EAGLE3:
case LLM_ARCH_MISTRAL4:
case LLM_ARCH_LLAMA_EMBED:
case LLM_ARCH_MAINCODER:
bool llama_model_has_encoder(const llama_model * model) {
switch (model->arch) {
- case LLM_ARCH_T5: return true;
- case LLM_ARCH_T5ENCODER: return true;
+ case LLM_ARCH_T5:
+ case LLM_ARCH_T5ENCODER:
+ case LLM_ARCH_EAGLE3: return true;
default: return false;
}
}
layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", bid), {n_embd_v_}, TENSOR_NOT_REQUIRED);
}
}
+
+const int32_t * llama_model_target_layer_ids(const struct llama_model * model) {
+ const auto & v = model->target_layer_ids;
+ return v.empty() ? nullptr : v.data();
+}
+
+uint32_t llama_model_target_layer_ids_n(const struct llama_model * model) {
+ return (uint32_t) model->target_layer_ids.size();
+}
struct ggml_tensor * per_layer_model_proj = nullptr;
struct ggml_tensor * per_layer_proj_norm = nullptr;
+ // eagle3
+ struct ggml_tensor * fc = nullptr; // feature fusion layer
+ struct ggml_tensor * d2t = nullptr; // draft to target vocabulary mapping
+
+ // unified vector to store target-model extracted layer ids in eagle3, dflash, etc.
+ std::vector<int32_t> target_layer_ids;
+
std::vector<llama_layer> layers;
//Dense linear projections for SentenceTransformers models like embeddinggemma
void tokenize(const std::string & text, std::vector<llama_token> & output) {
// normalize and split by whitespace
- std::vector<std::string> words = preprocess(text, vocab.get_normalizer_lowercase());
+ std::vector<std::string> words = preprocess(text, vocab.get_normalizer_opts());
// bos token prepended already
// find the longest tokens that form the words
}
// TODO: reduce string copies by using cpts_offs array
- static std::vector<std::string> preprocess(const std::string & text, bool lowercase) {
- const std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
+ static std::vector<std::string> preprocess(const std::string & text, const llama_vocab::normalizer_options & normalizer_opts) {
+ std::vector<uint32_t> cpts = unicode_cpts_from_utf8(text);
+ if (normalizer_opts.strip_accents) {
+ cpts = unicode_cpts_normalize_nfd(cpts);
+ }
std::vector<std::string> words(1, "");
- for (const uint32_t cpt : cpts_nfd) {
+ for (const uint32_t cpt : cpts) {
const auto flags = unicode_cpt_flags_from_cpt(cpt);
if (flags.is_whitespace) {
continue;
}
- const std::string s = unicode_cpt_to_utf8(lowercase ? unicode_tolower(cpt) : cpt);
+ if (normalizer_opts.strip_accents && flags.is_accent_mark) {
+ continue;
+ }
+
+ const std::string s = unicode_cpt_to_utf8(normalizer_opts.lowercase ? unicode_tolower(cpt) : cpt);
if (flags.is_punctuation || ( cpt < 0x7F && flags.is_symbol ) || is_chinese_char(cpt)) {
if (words.back().size()) { // finish previous word if any
words.emplace_back();
llm_tokenizer_whitespace_session(const llama_vocab & vocab, const llm_tokenizer_bpe & tokenizer) : llm_tokenizer_bpe_session{vocab, tokenizer}, vocab{vocab} {}
void tokenize(const std::string & text, std::vector<llama_token> & output) override {
- const bool lowercase = vocab.get_normalizer_lowercase();
+ const bool lowercase = vocab.get_normalizer_opts().lowercase;
std::string segment;
auto flush = [&]() {
bool remove_extra_whitespaces = false;
bool escape_whitespaces = true;
bool treat_whitespace_as_suffix = false;
- bool normalizer_lowercase = true; // Lowercase normalizer (tokenizer.json)
+
+ // BertNormalizer options
+ llama_vocab::normalizer_options normalizer_opts;
std::unordered_map<std::string, llama_token> token_to_id;
std::vector<token_data> id_to_token;
} else if (
tokenizer_pre == "whitespace") {
pre_type = LLAMA_VOCAB_PRE_TYPE_WHITESPACE;
- normalizer_lowercase = false;
+ normalizer_opts.lowercase = false;
} else if (
tokenizer_pre == "refact") {
pre_type = LLAMA_VOCAB_PRE_TYPE_REFACT;
}
}
- // Lowercase normalizer flag (consulted by WPM / whitespace BPE)
- ml.get_key(LLM_KV_TOKENIZER_NORMALIZER_LOWERCASE, normalizer_lowercase, false);
+ // BertNormalizer options
+ ml.get_key(LLM_KV_TOKENIZER_NORMALIZER_LOWERCASE, normalizer_opts.lowercase, false);
+ normalizer_opts.strip_accents = normalizer_opts.lowercase;
+ ml.get_key(LLM_KV_TOKENIZER_NORMALIZER_STRIP_ACCENTS, normalizer_opts.strip_accents, false);
// suppress tokens
{
return pimpl->treat_whitespace_as_suffix;
}
-bool llama_vocab::get_normalizer_lowercase() const {
- return pimpl->normalizer_lowercase;
+const llama_vocab::normalizer_options & llama_vocab::get_normalizer_opts() const {
+ return pimpl->normalizer_opts;
}
const std::vector<llama_token> & llama_vocab::get_suppress_tokens() const {
llama_token_attr attr;
};
+ struct normalizer_options {
+ bool lowercase = true;
+ bool strip_accents = true;
+ // TODO: clean_text, handle_chinese_chars
+ };
+
llama_vocab();
~llama_vocab();
bool get_remove_extra_whitespaces () const;
bool get_escape_whitespaces () const;
bool get_treat_whitespace_as_suffix() const;
- bool get_normalizer_lowercase () const;
+ const normalizer_options & get_normalizer_opts() const;
const std::vector<llama_token> & get_suppress_tokens() const;
GGML_ASSERT(b->ne[0] == 1 && b->ne[1] == H_v && b->ne[2] == n_tokens && b->ne[3] == n_seqs);
GGML_ASSERT(s->ne[0] == S_v && s->ne[1] == S_v && s->ne[2] == H_v && s->ne[3] == n_seqs);
- // K=1 (final state only): reshape to 3D (S_v*S_v*H_v, 1, n_seqs) for ggml_gated_delta_net.
- ggml_tensor * s_3d = ggml_reshape_3d(ctx0, s, S_v * S_v * H_v, 1, n_seqs);
- ggml_tensor * result = ggml_gated_delta_net(ctx0, q, k, v, g, b, s_3d);
+ // K=1: output carries the final state only. state s is 4D [S_v, S_v, H_v, n_seqs].
+ ggml_tensor * result = ggml_gated_delta_net(ctx0, q, k, v, g, b, s, /*K=*/1);
if (n_tokens == 1) {
cb(result, LLAMA_TENSOR_NAME_FGDN_AR, il);
} else {
const int64_t D = S_v * S_v * H_v;
const int64_t K = cparams.n_rs_seq + 1;
- // TODO: remove pad + simplify
- ggml_tensor * s_3d = ggml_reshape_3d(ctx0, s, D, 1, n_seqs);
- ggml_tensor * s_3d_pad = ggml_pad (ctx0, s_3d, 0, K - 1, 0, 0);
-
- ggml_tensor * gdn_out = ggml_gated_delta_net(ctx0, q, k, v, g, b, s_3d_pad);
+ // state s is 4D [S_v, S_v, H_v, n_seqs]; K snapshot slots are written into the output.
+ ggml_tensor * gdn_out = ggml_gated_delta_net(ctx0, q, k, v, g, b, s, K);
if (n_seq_tokens > 1) {
cb(gdn_out, LLAMA_TENSOR_NAME_FGDN_CH, il);
} else {
cb(output, "attn_output", il);
const size_t row_size = hparams.n_embd_s() * ggml_element_size(ssm_states_all);
- for (int64_t k_i = 0; k_i < K; ++k_i) {
- const uint32_t cache_slot = (uint32_t) (K - 1 - k_i);
- ggml_tensor * src = ggml_view_4d(ctx0, gdn_out,
- S_v, S_v, H_v, n_seqs,
- ggml_row_size(gdn_out->type, S_v),
- ggml_row_size(gdn_out->type, S_v * S_v),
- ggml_row_size(gdn_out->type, S_v * S_v * H_v),
- ggml_row_size(gdn_out->type, attn_score_elems + k_i * state_size_per_snap));
- ggml_tensor * dst = ggml_view_2d(ctx0, ssm_states_all,
- hparams.n_embd_s(), n_seqs, ssm_states_all->nb[1],
- ((size_t) cache_slot * mem_size + kv_head) * row_size);
+ // op writes the last min(n_seq_tokens, K) snapshots; trailing slots are left unwritten
+ const int64_t n_written = std::min<int64_t>(n_seq_tokens, K);
- ggml_build_forward_expand(gf, ggml_cpy(ctx0, src, dst));
- }
+ // write the produced snapshots into the recurrent cache (snapshot slot i -> rollback group i)
+ ggml_tensor * src = ggml_view_3d(ctx0, gdn_out,
+ D, n_seqs, n_written,
+ ggml_row_size(gdn_out->type, D),
+ ggml_row_size(gdn_out->type, state_size_per_snap),
+ ggml_row_size(gdn_out->type, attn_score_elems));
+
+ ggml_tensor * dst = ggml_view_3d(ctx0, ssm_states_all,
+ D, n_seqs, n_written,
+ ssm_states_all->nb[1],
+ (size_t) mem_size * row_size,
+ (size_t) kv_head * row_size);
+
+ ggml_build_forward_expand(gf, ggml_cpy(ctx0, src, dst));
return output;
}
--- /dev/null
+#include "models.h"
+
+void llama_model_eagle3::load_arch_hparams(llama_model_loader & ml) {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ if (!ml.get_arr(LLM_KV_TARGET_LAYERS, target_layer_ids, false)) {
+ throw std::runtime_error("EAGLE3 model requires 'extract_layers' in GGUF metadata");
+ }
+ if (target_layer_ids.size() != 3) {
+ throw std::runtime_error("EAGLE3 requires exactly 3 entries in 'extract_layers'");
+ }
+ LLAMA_LOG_INFO("%s: EAGLE3 extract_layers = [%d, %d, %d]\n", __func__,
+ target_layer_ids[0],
+ target_layer_ids[1],
+ target_layer_ids[2]);
+
+ uint32_t n_embd_tgt = 0;
+
+ ml.get_key(LLM_KV_TARGET_HIDDEN_SIZE, n_embd_tgt);
+ LLAMA_LOG_INFO("%s: EAGLE3 n_embd_tgt = %u (draft n_embd = %u)\n", __func__, n_embd_tgt, hparams.n_embd);
+
+ hparams.n_embd_inp_impl = (uint32_t) target_layer_ids.size() * n_embd_tgt;
+
+ // eagle3 norm_before_residual (optional, default false)
+ // compatible with Readhat eagle3 speculator model
+ ml.get_key(LLM_KV_NORM_BEFORE_RESIDUAL, hparams.norm_before_residual, false);
+ if (hparams.norm_before_residual) {
+ LLAMA_LOG_INFO("%s: EAGLE3gnorm_before_residual = true\n", __func__);
+ }
+
+ type = LLM_TYPE_UNKNOWN;
+}
+
+void llama_model_eagle3::load_arch_tensors(llama_model_loader &) {
+ LLAMA_LOAD_LOCALS;
+
+ const int64_t n_embd_inp = hparams.n_embd_inp();
+ const int64_t n_embd_attn_input = 2 * n_embd;
+
+ // Get vocab size from the d2t tensor in the GGUF file (optional - only needed if eagle3 has different vocab_size than target)
+ // d2t: draft to target vocabulary mapping
+ int64_t n_draft_vocab = n_vocab; // Default: same as target vocab
+ const struct ggml_tensor * d2t_meta = ml->get_tensor_meta("d2t");
+ if (d2t_meta) {
+ n_draft_vocab = d2t_meta->ne[0]; // update draft vocab size
+ d2t = create_tensor(tn(LLM_TENSOR_D2T), {n_draft_vocab}, 0);
+ LLAMA_LOG_INFO("%s: EAGLE3 using d2t mapping (draft_vocab_size = %lld)\n", __func__, (long long)n_draft_vocab);
+ } else {
+ d2t = nullptr; // no d2t, use default vocab size
+ LLAMA_LOG_INFO("%s: EAGLE3 without d2t - sharing same vocab_size with target (vocab_size = %lld)\n", __func__, (long long)n_draft_vocab);
+ }
+
+ // Feature fusion layer: projects 3 target layers to draft hidden size
+ fc = create_tensor(tn(LLM_TENSOR_FC, "weight"), {n_embd_inp, n_embd}, 0);
+
+ // Output layer (uses draft vocab size)
+ output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+ output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_draft_vocab}, TENSOR_NOT_REQUIRED);
+
+ // Token embeddings (optional - Llama 3.3 70B EAGLE3 has its own)
+ const struct ggml_tensor * tok_embd_meta = ml->get_tensor_meta(tn(LLM_TENSOR_TOKEN_EMBD, "weight").str().c_str());
+ if (tok_embd_meta) {
+ const int64_t n_target_vocab = tok_embd_meta->ne[1];
+ tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_target_vocab}, 0);
+ LLAMA_LOG_INFO("%s: EAGLE3 using its own token_embd (vocab = %lld)\n", __func__, (long long)n_target_vocab);
+ }
+
+ // Single decoder layer
+ for (int i = 0; i < n_layer; ++i) {
+ auto & layer = layers[i];
+
+ // input_layernorm: applied to token embeddings
+ layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+ // eagle3 specific: hidden_norm applied to fused target features
+ layer.attn_norm_2 = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, 0);
+
+ // Attention takes input_embeds_normed + fused_target_normed as input
+ layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd_attn_input, n_embd_head_k * n_head}, 0);
+ layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd_attn_input, n_embd_k_gqa}, 0);
+ layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd_attn_input, n_embd_v_gqa}, 0);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
+ layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
+
+ // rope_freqs for llama3 rope scaling (optional - only if eagle3 config has rope_scaling)
+ layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED);
+ }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_eagle3::build_arch_graph(const llm_graph_params & params) const {
+ switch (params.gtype) {
+ case LLM_GRAPH_TYPE_ENCODER:
+ return std::make_unique<graph<true>>(*this, params);
+ case LLM_GRAPH_TYPE_DEFAULT:
+ case LLM_GRAPH_TYPE_DECODER:
+ return std::make_unique<graph<false>>(*this, params);
+ default:
+ GGML_ABORT("invalid graph type");
+ };
+}
+
+template <>
+ggml_tensor * llama_model_eagle3::graph<true>::build_inp_embd_enc() const {
+ ggml_tensor * cur = nullptr;
+
+ // Input: Target model features (3 layers concatenated: low, mid, high)
+ // Data will be provided via ubatch->embd in encode_eagle3_features()
+ auto inp_target = std::make_unique<llm_graph_input_embd>(hparams.n_embd_inp());
+ inp_target->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32,hparams.n_embd_inp(), n_tokens);
+ ggml_set_input(inp_target->embd);
+
+ cur = inp_target->embd;
+ cb(cur, "inp_embd", -1);
+
+ res->add_input(std::move(inp_target));
+
+ return cur;
+}
+
+// eagle3 Encoder: processes target model features through feature fusion layer
+// Input: target_features e.g. [12288, n_tokens] from target model layers low, middle, high
+// Output: g_embeddings e.g. [4096, n_tokens] stored in context
+template <>
+llama_model_eagle3::graph<true>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+ ggml_tensor * cur = nullptr;
+
+ cur = build_inp_embd_enc();
+
+ // Feature fusion layer
+ cur = build_lora_mm(model.fc, cur);
+ cb(cur, "fc_out", -1);
+
+ // Output: g_embeddings e.g. [4096, n_tokens]
+ // store in t_h_nextn (same as MTP) so can be read via llama_get_embeddings_nextn(ctx_dft)
+ ggml_set_output(cur);
+ res->t_h_nextn = cur;
+
+ ggml_build_forward_expand(gf, cur);
+}
+
+// eagle3 Decoder: processes draft tokens using g_embeddings from encoder
+// Input: draft tokens + g_embeddings from encoder
+// Output: draft logits
+template <>
+llama_model_eagle3::graph<false>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+ const int64_t n_embd_head = hparams.n_embd_head_v();
+
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+ GGML_ASSERT(n_layer == 1); // eagle3 has only one decoder layer
+
+ ggml_tensor * cur;
+ ggml_tensor * inpL;
+
+ // eagle3 Decoder receives:
+ // 1. Token embeddings (e.g.from eagle3's own tok_embd for Llama 3.3 70B, or target model for Llama 3.1 8B)
+ // 2. g_embeddings from encoder
+ auto * tok_embd = model.tok_embd;
+ if (model.tok_embd == nullptr) {
+ GGML_ASSERT(cparams.ctx_other != nullptr);
+ const auto * model_other = llama_get_model(cparams.ctx_other);
+
+ GGML_ASSERT(model_other->tok_embd != nullptr && "EAGLE3 decoder requires token embeddings (own or from target model)");
+ tok_embd = model_other->tok_embd;
+ }
+
+ auto inp = std::make_unique<llm_graph_input_embd>(n_embd);
+
+ inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+ ggml_set_input(inp->tokens);
+
+ inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
+ ggml_set_input(inp->embd);
+
+ ggml_tensor * inp_embd = ggml_get_rows(ctx0, tok_embd, inp->tokens);
+ cb(inp_embd, "inp_embd", -1);
+
+ ggml_tensor * inp_g = inp->embd;
+ cb(inp_g, "inp_g_embeddings", -1);
+
+ res->add_input(std::move(inp));
+
+ inpL = inp_g;
+
+ // inp_pos - contains the positions
+ ggml_tensor * inp_pos = build_inp_pos();
+
+ auto * inp_attn = build_attn_inp_kv();
+
+ const float kq_scale = 1.0f/sqrtf(float(n_embd_head));
+
+ // Single decoder layer (il = 0)
+ const int il = 0;
+ {
+ // Apply input_layernorm to the token embeddings
+ ggml_tensor * embd_norm = build_norm(inp_embd,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(embd_norm, "embd_norm", il);
+
+ // Apply hidden_norm to inp_g
+ ggml_tensor * g_norm = build_norm(inp_g,
+ model.layers[il].attn_norm_2, NULL,
+ LLM_NORM_RMS, -1);
+ cb(g_norm, "g_norm", il);
+
+ // norm_before_residual: determines what goes into the residual connection (compatible with Readhat eagle3 speculator model)
+ // - false (default): use raw inp_g for residual
+ // - true: use normalized g_norm for residual
+ // inpL is the concatenated input (normalized inp_embd + normalized inp_g)
+ ggml_tensor * inpSA = hparams.norm_before_residual ? g_norm : inpL;
+
+ // Concatenate normalized inp_embd and normalized inp_g
+ cur = ggml_concat(ctx0, embd_norm, g_norm, il);
+ cb(cur, "concat_embd", il);
+
+ // Self-attention with concatenated input
+ ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+
+ ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+
+ ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, 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);
+ Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+ // rope freq factors, returns nullptr if not available
+ ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+ // RoPE
+ 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
+ );
+ 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(Qcur, "Qcur_rope", il);
+ cb(Kcur, "Kcur_rope", il);
+
+ cur = build_attn(inp_attn,
+ model.layers[il].wo, NULL, nullptr,
+ Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+
+ // Add residual and update it
+ ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // Apply FFN norm to the sum
+ cur = build_norm(ffn_inp,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "post_attn_norm", il);
+
+ cur = build_ffn(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, il);
+ cb(cur, "ffn_out", il);
+
+ // Output norm with residual
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cb(cur, "eagle3_prenorm", il);
+
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ // Output prenorm state (for next token's g_embeddings in autoregressive generation)
+ ggml_set_output(cur);
+ res->t_h_nextn = cur;
+
+ cur = build_norm(cur,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head - projects to draft vocabulary
+ // if the draft has no own output projection, inherit the target model's lm_head
+ auto * output = model.output;
+ if (output == nullptr) {
+ GGML_ASSERT(cparams.ctx_other != nullptr);
+ const auto * model_other = llama_get_model(cparams.ctx_other);
+
+ GGML_ASSERT(model_other->output != nullptr && "EAGLE3 decoder requires an output projection (own or from target model)");
+ output = model_other->output;
+ }
+ cur = build_lora_mm(output, cur);
+
+ if (model.d2t) {
+ const int64_t n_draft_vocab = cur->ne[0];
+ const int64_t n_outputs = cur->ne[1];
+ const int64_t n_vocab = (int64_t) model.vocab.n_tokens();
+
+ GGML_ASSERT(model.d2t->type == GGML_TYPE_I64);
+ GGML_ASSERT(model.d2t->ne[0] == n_draft_vocab);
+
+ ggml_tensor * logits = ggml_fill(ctx0, ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 1, n_vocab, n_outputs), -INFINITY);
+ cur = ggml_set_rows(ctx0, logits,
+ ggml_reshape_3d(ctx0, cur, 1, n_draft_vocab, n_outputs),
+ ggml_reshape_3d(ctx0, model.d2t, n_draft_vocab, 1, 1));
+ cur = ggml_reshape_2d(ctx0, cur, n_vocab, n_outputs);
+ }
+
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+
+ ggml_build_forward_expand(gf, cur);
+}
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }, 0);
+ create_tensor(tn(LLM_TENSOR_MASKED_EMBD_CENTROIDS, "weight"), {}, TENSOR_NOT_REQUIRED);
+ create_tensor(tn(LLM_TENSOR_MASKED_EMBD_ORDERING), {}, TENSOR_NOT_REQUIRED);
+
const int64_t n_embd_backbone = hparams.n_embd_inp();
nextn_proj_post = create_tensor(tn(LLM_TENSOR_NEXTN_PROJ_POST, "weight"), { n_embd, n_embd_backbone }, 0);
const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
const int n_rot_l = hparams.n_rot(il);
+ res->t_layer_inp[il] = inpL;
+
// norm
cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
+ res->t_layer_inp[il] = inpL;
+
ggml_tensor * inpSA = inpL;
// norm
ggml_tensor * s,
int il);
- // use the ggml_gated_delta_net fused operator (K=1; state has shape (D, 1, n_seqs))
+ // use the ggml_gated_delta_net fused operator (K=1; state has shape [S_v, S_v, H_v, n_seqs])
std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_fused(
ggml_tensor * q,
ggml_tensor * k,
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
+struct llama_model_eagle3 : public llama_model_base {
+ llama_model_eagle3(const struct llama_model_params & params) : llama_model_base(params) {}
+ void load_arch_hparams(llama_model_loader & ml) override;
+ void load_arch_tensors(llama_model_loader & ml) override;
+
+ template <bool is_enc>
+ struct graph : public llm_graph_context {
+ graph(const llama_model & model, const llm_graph_params & params);
+
+ ggml_tensor * build_inp_embd_enc() const;
+ };
+
+ std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
struct llama_model_mistral4 : public llama_model_deepseek2 {
llama_model_mistral4(const struct llama_model_params & params) : llama_model_deepseek2(params) {}
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
+ res->t_layer_inp[il] = inpL;
+
const float freq_base_l = model.get_rope_freq_base (cparams, il);
const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
ml.get_key(LLM_KV_SSM_TIME_STEP_RANK, hparams.ssm_dt_rank);
ml.get_key(LLM_KV_SSM_GROUP_COUNT, hparams.ssm_n_group);
+ // Load attention parameters
+ ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k_full, false);
+ ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v_full, false);
+
for (uint32_t i = 0; i < hparams.n_layer(); ++i) {
hparams.is_recr_impl[i] = hparams.n_head_kv(i) == 0;
}
GGML_ASSERT(n_seqs != 0);
GGML_ASSERT(ubatch.equal_seqs());
GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
- GGML_ASSERT(d_inner % n_head == 0);
+ GGML_ASSERT(d_inner % n_heads == 0);
GGML_ASSERT(n_group == 0);
ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
+ res->t_layer_inp[il] = inpL;
+
ggml_tensor * inpSA = inpL;
// norm
}
if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
- cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
+ res->t_layer_inp[il] = inpL;
+
ggml_tensor * inpSA = inpL;
// norm
overview / pkg / ggml / sources / whisper.cpp / commitdiff