ikawrakow/ik_llama.cpp
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Graph parallel for SEED-OSS (#1222)

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* Graph parallel for SEED-OSS

* Cleanup
This commit is contained in:
Kawrakow
2026-02-04 16:07:43 +02:00
committed by GitHub
parent 17d101863d
commit b41b8cf813
3 changed files with 25 additions and 72 deletions
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84
src/llama-build-context.cpp
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@@ -3513,13 +3513,14 @@ ggml_cgraph * llm_build_context::build_seedoss() {
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;
ggml_tensor * cur;
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();
ggml_tensor * inp_pos = build_inp_pos();
ggml_tensor * inp_out_ids = build_inp_out_ids();
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
@@ -3527,57 +3528,16 @@ ggml_cgraph * llm_build_context::build_seedoss() {
const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
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);
cur = build_std_attention(gf, model.layers[il].attn_norm, inpL, inp_pos, il == n_layer-1 ? inp_out_ids : nullptr, nullptr,
KQ_mask, nullptr, nullptr, kq_scale, 0.0f, 0, il, true, false, true);
// self-attention
{
auto [Qcur, Kcur, Vcur] = llm_build_mul_mat_qkv(gf, cur, model.layers[il].wq, model.layers[il].bq,
model.layers[il].wk, model.layers[il].bk,
model.layers[il].wv, model.layers[il].bv, 0.f, 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, kq_scale, cb, il);
}
if (il == n_layer - 1) {
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, cur, hparams, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, cb, il);
cb(cur, "attn_post_norm", il);
cur = llm_build_ffn(ctx0, lctx, model.layers[il].attn_post_norm, ffn_inp,
cur = llm_build_ffn(ctx0, lctx, model.layers[il].attn_post_norm, 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);
LLM_FFN_SILU, LLM_FFN_PAR, cb, il, gf, true);
cb(cur, "ffn_out", il);
cur = lctx.cvec.apply_to(ctx0, cur, il);
@@ -3587,13 +3547,7 @@ ggml_cgraph * llm_build_context::build_seedoss() {
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);
cur = build_output(lctx, ctx0, inpL, model.output, model.output_norm, cb);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
@@ -9478,8 +9432,8 @@ ggml_tensor * llm_build_context::build_std_attention(ggml_cgraph * gf, ggml_tens
GGML_ASSERT(bv->n_device == wq->n_device);
}
std::vector<ggml_tensor*> attn(wq->n_device, nullptr);
int id_last = -1;
bool output_bias_added = false;
bool input_added = false;
for (int id = 0; id < wq->n_device; ++id) {
int il_cb = 1000*(id+1) + il;
auto split_wq = wq->splits[id];
@@ -9492,6 +9446,7 @@ ggml_tensor * llm_build_context::build_std_attention(ggml_cgraph * gf, ggml_tens
(split_wq && split_wk && split_wv && split_wo && split_kl && split_vl));
if (!split_wq) continue;
auto cur = get_input_tensor_sm_graph(ctx0, input, id);
auto input_id = cur;
cur = do_split_norm(ctx0, cur, the_attn_norm, lctx.model.hparams, cb, id, il_cb, is_norm);
auto the_q_norm = model.layers[il].attn_q_norm ? model.layers[il].attn_q_norm->extra ?
((ggml_split_tensor_t *)model.layers[il].attn_q_norm->extra)->splits[id] : model.layers[il].attn_q_norm : nullptr;
@@ -9614,6 +9569,9 @@ ggml_tensor * llm_build_context::build_std_attention(ggml_cgraph * gf, ggml_tens
if (inp_out_ids) { // && ggml_nrows(inp_out_ids) > 1) {
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
if (add_input && !input_added) {
input_id = ggml_get_rows(ctx0, input_id, inp_out_ids);
}
cb(cur, "fa_get_rows", il_cb);
}
@@ -9628,21 +9586,15 @@ ggml_tensor * llm_build_context::build_std_attention(ggml_cgraph * gf, ggml_tens
cb(cur, "kqv_wo_biased", il_cb);
output_bias_added = true;
}
if (add_input && !input_added) {
cur = ggml_add(ctx0, cur, input_id);
input_added = true;
}
if (cur->ne[1] > 32 && lctx.cparams.reduce_type != GGML_TYPE_F32) {
cur = ggml_cast(ctx0, cur, lctx.cparams.reduce_type);
}
ggml_build_forward_expand(gf, cur);
attn[id] = cur;
id_last = id;
}
GGML_ASSERT(id_last >= 0);
if (add_input) {
if (inp_out_ids) { // && ggml_nrows(inp_out_ids) > 1) {
input = ggml_get_rows(ctx0, input, inp_out_ids);
cb(input, "sainp_get_rows", il);
}
attn[id_last] = ggml_add(ctx0, attn[id_last], input);
cb(attn[id_last], "attn_out_with_input", il);
}
auto cur = ggml_reduce(ctx0, attn.data(), wq->n_device, GGML_OP_ADD);
ggml_build_forward_expand(gf, cur);

12
src/llama-load-tensors.cpp
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@@ -1002,12 +1002,11 @@ bool create_tensors_helper::create_seedoss_tensors(const LLM_TN & tn) {
}
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 = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.attn_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
layer.wq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_qo_dim});
layer.wk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_kv_dim});
@@ -1015,11 +1014,12 @@ bool create_tensors_helper::create_seedoss_tensors(const LLM_TN & tn) {
layer.wo = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_qo_dim, n_embd});
// optional bias tensors
layer.bq = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_qo_dim}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.bk = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_kv_dim}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.bv = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_kv_dim}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.bq = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_qo_dim}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.bk = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_kv_dim}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.bv = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_kv_dim}, llama_model_loader::TENSOR_NOT_REQUIRED);
layer.attn_post_norm = create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd});
layer.attn_post_norm = create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd});
layer.ffn_norm = layer.attn_post_norm;
create_std_ffn(i, tn, layer, n_ff, n_embd, ctx_split);
}

1
src/llama.cpp
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@@ -1755,6 +1755,7 @@ static bool is_model_split_supported(const llama_model & model) {
LLM_ARCH_OPENAI_MOE,
LLM_ARCH_ERNIE4_5_MOE,
LLM_ARCH_MINIMAX_M2,
LLM_ARCH_SEED_OSS,
};
auto it = k_supported.find(model.arch);
return it != k_supported.end();