ikawrakow/ik_llama.cpp
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Remove the glm45 graph building changes

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Kawrakow
2026-01-25 06:28:53 +00:00
parent 4d5dcba7c9
commit d08481d0f4
1 changed files with 31 additions and 78 deletions
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109
src/llama-build-context.cpp
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@@ -1337,40 +1337,6 @@ llm_expert_gating_func_type gating_op,
return cur;
}
static ggml_tensor * build_glm45_fa(ggml_context * ctx, ggml_tensor * q, ggml_tensor * k, ggml_tensor * v,
ggml_tensor * kq_mask, float kq_scale, bool should_use_f32_precision) {
auto ne1 = 8*v->ne[0];
auto ne2 = 4*v->ne[0];
ggml_tensor *q1, *q2;
if (q->ne[1] == 1 && k->ne[2] == 1) {
q1 = ggml_view_3d(ctx, q, q->ne[0], 1, 8, q->nb[1], q->nb[2], 0);
q2 = ggml_view_3d(ctx, q, q->ne[0], 1, 4, q->nb[1], q->nb[2], 8*q->ne[0]*ggml_element_size(q));
} else {
q1 = ggml_view_3d(ctx, q, q->ne[0], 8, k->ne[2]*q->ne[1], q->nb[2], q->nb[1]/k->ne[2], 0);
q2 = ggml_view_3d(ctx, q, q->ne[0], 4, k->ne[2]*q->ne[1], q->nb[2], q->nb[1]/k->ne[2], 8*q->ne[0]*ggml_element_size(q));
q1 = ggml_reshape_3d(ctx, ggml_cont(ctx, q1), q->ne[0], 8*k->ne[2], q->ne[1]);
q2 = ggml_reshape_3d(ctx, ggml_cont(ctx, q2), q->ne[0], 4*k->ne[2], q->ne[1]);
q1 = ggml_permute(ctx, q1, 0, 2, 1, 3);
q2 = ggml_permute(ctx, q2, 0, 2, 1, 3);
}
auto fa1 = ggml_flash_attn_ext(ctx, q1, k, v, kq_mask, kq_scale, 0.0f, 0.0f);
if (should_use_f32_precision) {
ggml_flash_attn_ext_set_prec(fa1, GGML_PREC_F32);
}
fa1 = ggml_reshape_2d(ctx, fa1, ne1, ggml_nelements(fa1)/ne1);
auto fa2 = ggml_flash_attn_ext(ctx, q2, k, v, kq_mask, kq_scale, 0.0f, 0.0f);
if (should_use_f32_precision) {
ggml_flash_attn_ext_set_prec(fa2, GGML_PREC_F32);
}
fa2 = ggml_reshape_2d(ctx, fa2, ne2, ggml_nelements(fa2)/ne2);
return ggml_concat(ctx, fa1, fa2, 0);
}
static ggml_tensor * llm_build_kqv(
struct ggml_context * ctx,
struct llama_context & lctx,
@@ -1441,27 +1407,20 @@ static ggml_tensor * llm_build_kqv(
0);
cb(v, "v", il);
//if (q->ne[1] == 1 && k->ne[1] >= 8192 && q->ne[2] / k->ne[2] == 12 && !sinks && n_swa == 0 &&
// k->view_src && k->view_src->buffer && !ggml_backend_buffer_is_host(k->view_src->buffer) &&
// k->type == GGML_TYPE_F16 && v->type == GGML_TYPE_F16) {
// cur = build_glm45_fa(ctx, q, k, v, kq_mask, kq_scale, should_use_f32_precision);
//} else {
cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias,
hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f);
ggml_flash_attn_ext_add_sinks(cur, sinks);
if (n_swa > 0) {
((int32_t *)cur->op_params)[4] = n_swa;
}
cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias,
hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f);
ggml_flash_attn_ext_add_sinks(cur, sinks);
if (n_swa > 0) {
((int32_t *)cur->op_params)[4] = n_swa;
}
// Some models produced NaNs/gibberish when FA is computed with f16 precision on CUDA
// For DeepSeek-2, it is perfectly fine with fp16 for PP, but I get gibberish when uding fp16 for TG.
// Not sure if it is really a matter of insufficient precision, or I have made a mistake in the fattn-vec-f16 kernel.
if (should_use_f32_precision) {
ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
}
//ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
//}
// Some models produced NaNs/gibberish when FA is computed with f16 precision on CUDA
// For DeepSeek-2, it is perfectly fine with fp16 for PP, but I get gibberish when uding fp16 for TG.
// Not sure if it is really a matter of insufficient precision, or I have made a mistake in the fattn-vec-f16 kernel.
if (should_use_f32_precision) {
ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
}
//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 {
@@ -9390,30 +9349,24 @@ ggml_tensor * llm_build_context::build_std_attention(ggml_cgraph * gf, ggml_tens
ggml_row_size(split_vl->type, n_embd_head_v), 0);
cb(v, "v", il_cb);
//if (q->ne[1] == 1 && k->ne[1] >= 65536/k->ne[2] && q->ne[2] / k->ne[2] == 12 && !sinks && n_swa == 0 &&
// k->view_src && k->view_src->buffer && !ggml_backend_buffer_is_host(k->view_src->buffer) &&
// k->type == GGML_TYPE_F16 && v->type == GGML_TYPE_F16) {
// cur = build_glm45_fa(ctx0, q, k, v, KQ_mask, KQ_scale, should_use_f32_precision);
//} else {
cur = ggml_flash_attn_ext(ctx0, q, k, v, KQ_mask, KQ_scale, hparams.f_max_alibi_bias,
hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f);
cb(cur, "flash_attn", il_cb);
if (model.layers[il].attn_sinks && model.layers[il].attn_sinks->extra) {
auto split = (ggml_split_tensor_t *)model.layers[il].attn_sinks->extra;
GGML_ASSERT(split->n_device == wq->n_device);
GGML_ASSERT(split->splits[id]);
ggml_flash_attn_ext_add_sinks(cur, split->splits[id]);
} else {
ggml_flash_attn_ext_add_sinks(cur, sinks);
}
if (n_swa > 0) {
((int32_t *)cur->op_params)[4] = n_swa;
}
// Some models produced NaNs/gibberish when FA is computed with f16 precision on CUDA
if (should_use_f32_precision) {
ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
}
//}
cur = ggml_flash_attn_ext(ctx0, q, k, v, KQ_mask, KQ_scale, hparams.f_max_alibi_bias,
hparams.attn_soft_cap ? hparams.f_attn_logit_softcapping : 0.0f);
cb(cur, "flash_attn", il_cb);
if (model.layers[il].attn_sinks && model.layers[il].attn_sinks->extra) {
auto split = (ggml_split_tensor_t *)model.layers[il].attn_sinks->extra;
GGML_ASSERT(split->n_device == wq->n_device);
GGML_ASSERT(split->splits[id]);
ggml_flash_attn_ext_add_sinks(cur, split->splits[id]);
} else {
ggml_flash_attn_ext_add_sinks(cur, sinks);
}
if (n_swa > 0) {
((int32_t *)cur->op_params)[4] = n_swa;
}
// Some models produced NaNs/gibberish when FA is computed with f16 precision on CUDA
if (should_use_f32_precision) {
ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
}
cur = ggml_reshape_2d(ctx0, cur, split_wo->ne[0], n_tokens);
cb(cur, "flash_attn_reshaped", il_cb);