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llama.cpp
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fixed tensor mappings and working on buildin graph

This commit is contained in:
ryan-mangeno 2025-08-27 15:32:20 -04:00
parent 4ceb828112
commit 18c0c23ed8
3 changed files with 53 additions and 22 deletions
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3
ggml/src/ggml.c
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@ -3015,9 +3015,6 @@ struct ggml_tensor * ggml_mul_mat(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b) {
printf("Up: {%lld, %lld}\n", a->ne[0], a->ne[1]);
printf("Cur: {%lld, %lld}\n", b->ne[0], b->ne[1]);
GGML_ASSERT(ggml_can_mul_mat(a, b));
GGML_ASSERT(!ggml_is_transposed(a));

4
gguf-py/gguf/tensor_mapping.py
View File

@ -367,7 +367,7 @@ class TensorNameMap:
"model.layers.{bid}.mlp.up_proj", # llama-hf refact nemotron olmo2
"layers.{bid}.feed_forward.w3", # llama-pth
"encoder.layer.{bid}.intermediate.dense", # bert
"layers.{bid}.mlp.Wo", # modern bert
"layers.{bid}.mlp.Wi", # modern bert
"transformer.layer.{bid}.ffn.lin1", # distillbert
"transformer.h.{bid}.mlp.fc_in", # gpt-j
"transformer.h.{bid}.mlp.linear_3", # refact
@ -467,7 +467,7 @@ class TensorNameMap:
"model.layers.{bid}.mlp.down_proj", # llama-hf nemotron olmo2
"layers.{bid}.feed_forward.w2", # llama-pth
"encoder.layer.{bid}.output.dense", # bert
"layers.{bid}.mlp.Wi", # modern bert
"layers.{bid}.mlp.Wo", # modern bert
"transformer.layer.{bid}.ffn.lin2", # distillbert
"transformer.h.{bid}.mlp.fc_out", # gpt-j
"language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon

68
src/llama-model.cpp
View File

@ -2708,8 +2708,8 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
layer.wqkv = create_tensor(tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, 3 * n_embd }, 0);
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_ff, n_embd}, 0); // [3072, 384]
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, 2 * n_ff}, 0);
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, 2 * n_ff}, 0);
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
}
} break;
@ -7548,6 +7548,7 @@ struct llm_build_modern_bert : public llm_graph_context {
const int64_t n_embd_head = hparams.n_embd_head_v;
const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); // == n_head_kv * n_embd_head
const int64_t n_tokens = ubatch.n_tokens;
const int64_t n_ff = hparams.n_ff();
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
@ -7667,30 +7668,63 @@ struct llm_build_modern_bert : public llm_graph_context {
// MLP (prefer GEGLU if gate exists or up has 2*n_ff rows)
ggml_tensor * mlp_out = nullptr;
const bool has_gate_tensor = (model.layers[il].ffn_gate != nullptr);
const bool up_is_2x = (model.layers[il].ffn_up && model.layers[il].ffn_up->ne[0] == 2*hparams.n_ff());
ggml_tensor * ffn_gate_view = model.layers[il].ffn_gate;
ggml_tensor * ffn_up_view = model.layers[il].ffn_up;
if (ffn_gate_view == nullptr && ffn_up_view) {
// Case A: weight stored as (2*ffn, hidden) -> split rows into two (ffn x hidden)
if( ffn_up_view->ne[0] == 2 * n_ff and ffn_up_view->ne[1] == n_embd) {
// top half, (ffn up)
ffn_up_view = ggml_view_2d(ctx0, model.layers[il].ffn_up,
/*ne0*/ n_ff, /*ne1*/ n_embd,
/*nb1*/ model.layers[il].ffn_up->nb[1],
/*offset_bytes*/ (size_t)0);
// bottom half (gate)
ffn_gate_view = ggml_view_2d(ctx0, model.layers[il].ffn_up,
/*ne0*/ n_ff, /*ne1*/ n_embd,
/*nb1*/ model.layers[il].ffn_up->nb[1],
/*offset_bytes*/ (size_t)n_ff * model.layers[il].ffn_up->nb[1]);
}
else if ( ffn_up_view->ne[0] == n_embd && ffn_up_view->ne[1] == 2 * n_ff) {
// top half
ffn_up_view = ggml_view_2d(ctx0, model.layers[il].ffn_up,
n_embd, n_ff,
model.layers[il].ffn_up->nb[1],
0);
ffn_up_view = ggml_cont(ctx0, ffn_up_view);
ffn_gate_view = ggml_view_2d(ctx0, model.layers[il].ffn_up,
n_embd, n_ff,
model.layers[il].ffn_up->nb[1],
n_ff * sizeof(float));
ffn_gate_view = ggml_cont(ctx0, ffn_gate_view);
}
ggml_tensor * ffn_down_view = model.layers[il].ffn_down;
LLAMA_LOG_INFO("ffn shapes: Up: {%lld, %lld}, Gate: {%lld, %lld}, Down: {%lld, %lld}",
ffn_up_view->ne[0], ffn_up_view->ne[1], ffn_gate_view->ne[0], ffn_gate_view->ne[1], ffn_down_view->ne[0], ffn_down_view->ne[1]);
if (has_gate_tensor || up_is_2x) {
mlp_out = build_ffn(
h,
model.layers[il].ffn_up, /*up_b*/ NULL, /*up_shexp*/ NULL,
model.layers[il].ffn_gate, /*gate_b*/ NULL, /*gate_shexp*/ NULL,
ffn_gate_view , /*gate_b*/ NULL, /*gate_shexp*/ NULL,
model.layers[il].ffn_down, /*down_b*/ NULL, /*down_shexp*/ NULL,
/*expert_scores*/ NULL,
LLM_FFN_GEGLU, LLM_FFN_PAR, il);
cb(mlp_out, "ffn_out_geglu", il);
LLM_FFN_GEGLU, LLM_FFN_PAR, il
);
cb(mlp_out, "ffn_out_geglu", il);
} else {
LLAMA_LOG_INFO("Ffn_up : {%lld, %lld}, ffn_down : {%lld, %lld}\n", model.layers[il].ffn_up->ne[0], model.layers[il].ffn_up->ne[1],
model.layers[il].ffn_down->ne[0], model.layers[il].ffn_down->ne[0]);
mlp_out = build_ffn(
h,
model.layers[il].ffn_up, /*up_b*/ NULL, /*up_shexp*/ NULL,
/*gate*/ NULL, /*gate_b*/ NULL, /*gate_shexp*/ NULL,
model.layers[il].ffn_down, /*down_b*/ NULL, /*down_shexp*/ NULL,
/*expert_scores*/ NULL,
LLM_FFN_GELU, LLM_FFN_SEQ, il);
cb(mlp_out, "ffn_out_gelu", il);
model.layers[il].ffn_up, NULL, NULL,
model.layers[il].ffn_gate, NULL, NULL,
model.layers[il].ffn_down, NULL, NULL,
NULL,
LLM_FFN_GEGLU, LLM_FFN_PAR, il
);
cb(mlp_out, "ffn_out_geglu", il);
}
// Residual after MLP