llama-cpp-turboquant/src/models/modern-bert.cpp
Ryan Mangeno dfc959b886
model : Granite Embedding support (#15641)
ModernBERT but without `head.norm` so will currently fail to convert and run any other ModernBERT models, PRs with `head.norm` support welcome!

* constants and tensor mappings for modern bert support, model not supported yet but working on getting conversion to work for encoder only

* conversion now working, hf -> gguf

* working on support, now working on building graph

* some cleanup

* cleanup

* continuing

* correct tensor shape for qkv

* fixed tensor mappings and working on buildin graph

* tensor debugging now works -> (llama-eval-callback), instead of simulated gate split with views, GEGLU is now used which does exactly this

* cleanup

* cleanup

* cleanup

* more cleanup

* ubatch issues, the assert for checking equal seqs in llama-graph.cpp when building attention  keeps failing, setting ubatch size to 1 when running llama-embedding with --ubatch-size 1 makes it work, but needs to be looked into more

* added cls token per previous modern bert attempt, still working on checking out the rest

* fixed pre tokenizer and still working through previous pr

* working through previous attemp, implimented more accurate conversion per previous attempt, added local sliding window attention that alternates every third layer

* fixed pre tokenizer

* working on swa with local and global alternating attention

* some cleanup and now fails on build attn

* starting to work, and some cleanup, currently failing on last layer construction in graph build

* alternating rope implemented and modern bert graph build succeeds

* fixed asser for equal ubatch seq

* cleanup

* added mask check in vocab

* fixed alternating rope, the hparams.rope_freq_base_train and hparams.rope_freq_base_train_swa were the same and i set them to correct values

* reuse variable

* removed repeat

* standard swa method can be used instead of a new enum being LLAMA_SWA_TYPE_LOCAL

* correct swa layer indexing, is supposed to be 0, 3, 6 ... instead of 1, 4, 7 ...

* more modular hparam setting

* replaced attn out norm with ffn_norm and cosine similarity between hf embds and llama.cpp embds went way up, from 0.05 to 0.24, replaced the cacheless kv with swa todo per the previous conversion

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update convert_hf_to_gguf_update.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-vocab.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/tensor_mapping.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-graph.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-arch.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* removed redundant hparam set

* enums for model sizes

* conversion for modern-bert model supported rather than just granite-small

* Update src/llama-model.cpp

Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>

* Update src/llama-model.cpp

Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>

* fixed ordering of enum for freq_base_swa

* fixed where I added residual, now gives much much better embeddings~

* readded cacheless logic

* removing whitespace

* conversion now working for swa pattern - dense every n layers

* modern bert put into seperate src file

* removing whitespace

* fixed whitespace and newline errors in editorconfig job

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* better naming convention, n_swa_pattern -> swa_period

* reusing sliding_window_pattern key rather than making new dense_every_n_layers key, and adding writing and reading support

* fixing pyright type-check fail

* Update convert_hf_to_gguf.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/gguf_writer.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-hparams.h

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model-saver.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/models/modern-bert.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/models/modern-bert.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/models/modern-bert.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update gguf-py/gguf/gguf_writer.py

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/models/modern-bert.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/models/modern-bert.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model-loader.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model-loader.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model-loader.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* added descriptions in llama-model

* fixed tensor mappings for conversion

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update src/llama-model.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* mapping name for size

* nits

* unused

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>
2025-12-23 00:28:19 +01:00

126 lines
4.1 KiB
C++

#include "models.h"
template <bool iswa>
llm_build_modern_bert<iswa>::llm_build_modern_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
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_tensor * cur;
ggml_tensor * inpL;
ggml_tensor * inp_pos = build_inp_pos();
// construct input embeddings (token, type, position)
inpL = build_inp_embd(model.tok_embd);
cb(inpL, "inp_embd", -1);
// embed layer norm
inpL = build_norm(inpL, model.tok_norm, nullptr, LLM_NORM, -1);
cb(inpL, "inp_norm", -1);
ggml_tensor * inp_out_ids = build_inp_out_ids();
auto * inp_attn = build_attn_inp_no_cache();
for (int il = 0; il < n_layer; ++il) {
float freq_base_l = 0.0f;
if constexpr (iswa) {
freq_base_l = model.get_rope_freq_base(cparams, il);
} else {
freq_base_l = freq_base;
}
cur = inpL;
// attention layer norm
if (model.layers[il].attn_norm) {
cur = build_norm(inpL,
model.layers[il].attn_norm, NULL,
LLM_NORM, il);
cb(cur, "attn_norm", il);
}
// self attention
cur = build_lora_mm(model.layers[il].wqkv, cur);
cb(cur, "wqkv", il);
const size_t type_size = ggml_type_size(cur->type);
ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, n_embd_head*type_size, cur->nb[1], 0*type_size*(n_embd));
ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*type_size, cur->nb[1], 1*type_size*(n_embd));
ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*type_size, cur->nb[1], 1*type_size*(n_embd + n_embd_gqa));
// RoPE
Qcur = ggml_rope_ext(
ctx0, Qcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
Kcur = ggml_rope_ext(
ctx0, Kcur, inp_pos, nullptr,
n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale,
ext_factor, attn_factor, beta_fast, beta_slow
);
cb(Qcur, "Qcur", il);
cb(Kcur, "Kcur", il);
cb(Vcur, "Vcur", il);
cur = build_attn(inp_attn,
model.layers[il].wo, nullptr,
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
cb(cur, "kqv_out", il);
if (il == n_layer - 1 && 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
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
cb(ffn_inp, "ffn_inp", il);
// attention layer norm
cur = build_norm(ffn_inp,
model.layers[il].ffn_norm, NULL,
LLM_NORM, il);
cb(cur, "ffn_norm", il);
cur = build_ffn(cur,
model.layers[il].ffn_up, NULL, NULL,
NULL, NULL, NULL,
model.layers[il].ffn_down, NULL, NULL,
NULL,
LLM_FFN_GEGLU, LLM_FFN_SEQ, il);
// attentions bypass the intermediate layer
cur = ggml_add(ctx0, cur, ffn_inp);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = build_norm(cur,
model.output_norm, NULL,
LLM_NORM, -1);
cb(cur, "final_norm_out", -1);
if (hparams.pooling_type == LLAMA_POOLING_TYPE_CLS) {
// extracting cls token
cur = ggml_view_1d(ctx0, cur, hparams.n_embd, 0);
cb(cur, "cls_pooled_embd", -1);
}
cb(cur, "res_embd", -1);
res->t_embd = cur;
ggml_build_forward_expand(gf, cur);
}
// Explicit template instantiations
template struct llm_build_modern_bert<false>;
template struct llm_build_modern_bert<true>;