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standardize tts linting and formatting

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Concedo 2025-08-17 14:11:30 +08:00
parent cfc1a0d4ef
commit 9935ac093f
24 changed files with 371 additions and 355 deletions
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40
otherarch/ttscpp/src/orpheus_model.cpp
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@ -150,7 +150,7 @@ orpheus_context * build_new_orpheus_context(orpheus_model * model, int n_threads
return octx;
}
void orpheus_runner::orpheus_kv_cache_init() {
void orpheus_runner::orpheus_kv_cache_init() {
ggml_backend_buffer_type_t buft = nullptr;
if (octx->backend != nullptr) {
#ifdef GGML_USE_METAL
@ -192,21 +192,21 @@ void orpheus_runner::orpheus_kv_cache_init() {
}
void orpheus_runner::orpheus_build_kv_store(struct ggml_context * ctx, struct ggml_cgraph * graph, struct ggml_tensor * k_cur, struct ggml_tensor * v_cur, int index, uint32_t n_tokens, int repeat) {
k_cur = ggml_rope_ext(ctx, ggml_cont(ctx, ggml_reshape_3d(ctx, k_cur, model->head_size, model->n_kv_attn_heads, n_tokens)), octx->positions, model->rope_frequencies,
k_cur = ggml_rope_ext(ctx, ggml_cont(ctx, ggml_reshape_3d(ctx, k_cur, model->head_size, model->n_kv_attn_heads, n_tokens)), octx->positions, model->rope_frequencies,
model->head_size, 2,0, 500000.0f,
1.0f, 0.0f, 1.0f, 0.0f, 0.0f);
// A performance comparison between this method, i.e. performing 3 incremental copy operations in order to achieve repeat_interleave,
// and performing the repeat operation upfront before performign a single copy needs to be performed in order to better optimize this function.
// Additionally, it might be more performant for the values transposition to be performed prior to appending it to the cache, as it would save us
// Additionally, it might be more performant for the values transposition to be performed prior to appending it to the cache, as it would save us
// from incrementally larger transpositions with generation.
for (int i = 0; i < repeat; i++) {
struct ggml_tensor * k_cache_view = ggml_view_3d(
ctx,
kv_self->k_l[index],
ctx,
kv_self->k_l[index],
model->head_size,
model->n_kv_attn_heads,
n_tokens,
n_tokens,
ggml_element_size(kv_self->k_l[index]) * model->head_size * repeat,
ggml_element_size(kv_self->k_l[index]) * model->n_attn_heads * model->head_size,
ggml_element_size(kv_self->k_l[index]) * model->n_attn_heads * model->head_size * octx->current_position + i * ggml_element_size(kv_self->k_l[index]) * model->head_size
@ -230,19 +230,19 @@ void orpheus_runner::orpheus_kv_cache_init() {
struct ggml_cgraph * orpheus_runner::build_orpheus_graph(orpheus_ubatch & batch) {
init_build();
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx, 8192, false);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
const int32_t full_sequence_length = octx->current_position + (uint32_t) batch.n_tokens;
octx->positions = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens);
ggml_set_input(octx->positions);
octx->inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens);
ggml_set_input(octx->inp_tokens);
inpL = ggml_get_rows(ctx, model->embd, octx->inp_tokens);
struct ggml_tensor * KQ_mask_dec = build_attn_mask(ctx, octx, batch);
for (int l = 0; l < model->n_layers; l++) {
struct ggml_tensor * residual = inpL;
cur = orpheus_build_layer_norm(ctx, inpL, model->layers[l].input_norm);
@ -261,8 +261,8 @@ struct ggml_cgraph * orpheus_runner::build_orpheus_graph(orpheus_ubatch & batch)
model->head_size, full_sequence_length, model->n_attn_heads,
ggml_element_size(kv_self->k_l[l]) * model->n_attn_heads * model->head_size,
ggml_element_size(kv_self->k_l[l]) * model->head_size,
0));
0));
struct ggml_tensor * v =
ggml_view_2d(ctx, kv_self->v_l[l],
model->hidden_size, full_sequence_length,
@ -272,7 +272,7 @@ struct ggml_cgraph * orpheus_runner::build_orpheus_graph(orpheus_ubatch & batch)
v = ggml_cont_3d(ctx, ggml_transpose(ctx, v), full_sequence_length, model->head_size, model->n_attn_heads);
Qcur = ggml_rope_ext(
ctx, ggml_cont(ctx, ggml_reshape_3d(ctx, Qcur, model->head_size, model->n_attn_heads, batch.n_tokens)),
ctx, ggml_cont(ctx, ggml_reshape_3d(ctx, Qcur, model->head_size, model->n_attn_heads, batch.n_tokens)),
octx->positions, model->rope_frequencies, model->head_size, 2, 0, 500000.0f, // rope theta
1.0f, 0.0f, 1.0f, 0.0f, 0.0f);
@ -286,7 +286,7 @@ struct ggml_cgraph * orpheus_runner::build_orpheus_graph(orpheus_ubatch & batch)
}
cur = ggml_add(ctx, attn_out, residual);
struct ggml_tensor * residualffn = cur;
// mlp
@ -298,7 +298,7 @@ struct ggml_cgraph * orpheus_runner::build_orpheus_graph(orpheus_ubatch & batch)
cur = ggml_add(ctx, cur, residualffn);
inpL = cur;
}
cur = orpheus_build_layer_norm(ctx, cur, model->output_norm);
// only about 40k of the output head is actually uses for generation purposes. Ideally the head tensor should be shrunk and sampled tokens should be incremented.
cur = ggml_mul_mat(ctx, model->head, cur);
@ -307,15 +307,15 @@ struct ggml_cgraph * orpheus_runner::build_orpheus_graph(orpheus_ubatch & batch)
}
ggml_build_forward_expand(gf, cur);
free_build();
return gf;
}
void orpheus_runner::decode(orpheus_ubatch & batch) {
ggml_backend_sched_reset(octx->sched);
octx->output_tokens.reserve(model->max_generation_size);
const size_t new_size = model->vocab_size * model->max_generation_size * sizeof(float);
octx->prep_output_buffer(new_size);
@ -324,10 +324,10 @@ void orpheus_runner::decode(orpheus_ubatch & batch) {
// the output is always the last tensor in the graph
struct ggml_tensor * res = gf->nodes[gf->n_nodes - 1];
ggml_backend_sched_alloc_graph(octx->sched, gf);
set_inputs(batch);
ggml_backend_sched_graph_compute_async(octx->sched, gf);
float * logits_out = octx->logits + octx->n_outputs * model->vocab_size;
octx->get_ggml_node_data(res, logits_out, model->vocab_size * sizeof(float));