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Oct 28, 2025
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unify the naming style of ggml extension functions #921

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12 changes: 6 additions & 6 deletions clip.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -641,10 +641,10 @@ class CLIPVisionEmbeddings : public GGMLBlock {
// concat(patch_embedding, class_embedding) + position_embedding
struct ggml_tensor* patch_embedding;
int64_t N = pixel_values->ne[3];
patch_embedding = ggml_nn_conv_2d(ctx, pixel_values, patch_embed_weight, nullptr, patch_size, patch_size); // [N, embed_dim, image_size // pacht_size, image_size // pacht_size]
patch_embedding = ggml_reshape_3d(ctx, patch_embedding, num_patches, embed_dim, N); // [N, embed_dim, num_patches]
patch_embedding = ggml_cont(ctx, ggml_permute(ctx, patch_embedding, 1, 0, 2, 3)); // [N, num_patches, embed_dim]
patch_embedding = ggml_reshape_4d(ctx, patch_embedding, 1, embed_dim, num_patches, N); // [N, num_patches, embed_dim, 1]
patch_embedding = ggml_ext_conv_2d(ctx, pixel_values, patch_embed_weight, nullptr, patch_size, patch_size); // [N, embed_dim, image_size // pacht_size, image_size // pacht_size]
patch_embedding = ggml_reshape_3d(ctx, patch_embedding, num_patches, embed_dim, N); // [N, embed_dim, num_patches]
patch_embedding = ggml_cont(ctx, ggml_permute(ctx, patch_embedding, 1, 0, 2, 3)); // [N, num_patches, embed_dim]
patch_embedding = ggml_reshape_4d(ctx, patch_embedding, 1, embed_dim, num_patches, N); // [N, num_patches, embed_dim, 1]

struct ggml_tensor* class_embedding = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, embed_dim, N);
class_embedding = ggml_repeat(ctx, class_embed_weight, class_embedding); // [N, embed_dim]
Expand Down Expand Up @@ -736,7 +736,7 @@ class CLIPTextModel : public GGMLBlock {
auto text_projection = params["text_projection"];
ggml_tensor* pooled = ggml_view_1d(ctx, x, hidden_size, x->nb[1] * max_token_idx);
if (text_projection != nullptr) {
pooled = ggml_nn_linear(ctx, pooled, text_projection, nullptr);
pooled = ggml_ext_linear(ctx, pooled, text_projection, nullptr);
} else {
LOG_DEBUG("identity projection");
}
Expand Down Expand Up @@ -836,7 +836,7 @@ class CLIPProjection : public UnaryBlock {
if (transpose_weight) {
w = ggml_cont(ctx, ggml_transpose(ctx, w));
}
return ggml_nn_linear(ctx, x, w, nullptr);
return ggml_ext_linear(ctx, x, w, nullptr);
}
};

Expand Down
8 changes: 4 additions & 4 deletions common.hpp
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Expand Up @@ -205,8 +205,8 @@ class GEGLU : public UnaryBlock {
auto gate_b = ggml_view_1d(ctx, b, b->ne[0] / 2, b->nb[0] * b->ne[0] / 2); // [dim_out, ]

auto x_in = x;
x = ggml_nn_linear(ctx, x_in, x_w, x_b); // [ne3, ne2, ne1, dim_out]
auto gate = ggml_nn_linear(ctx, x_in, gate_w, gate_b); // [ne3, ne2, ne1, dim_out]
x = ggml_ext_linear(ctx, x_in, x_w, x_b); // [ne3, ne2, ne1, dim_out]
auto gate = ggml_ext_linear(ctx, x_in, gate_w, gate_b); // [ne3, ne2, ne1, dim_out]

gate = ggml_gelu_inplace(ctx, gate);

Expand Down Expand Up @@ -325,7 +325,7 @@ class CrossAttention : public GGMLBlock {
auto k = to_k->forward(ctx, context); // [N, n_context, inner_dim]
auto v = to_v->forward(ctx, context); // [N, n_context, inner_dim]

x = ggml_nn_attention_ext(ctx, backend, q, k, v, n_head, nullptr, false, false, flash_attn); // [N, n_token, inner_dim]
x = ggml_ext_attention_ext(ctx, backend, q, k, v, n_head, nullptr, false, false, flash_attn); // [N, n_token, inner_dim]

x = to_out_0->forward(ctx, x); // [N, n_token, query_dim]
return x;
Expand Down Expand Up @@ -492,7 +492,7 @@ class AlphaBlender : public GGMLBlock {
float get_alpha() {
// image_only_indicator is always tensor([0.]) and since mix_factor.shape is [1,]
// so learned_with_images is same as learned
float alpha = ggml_backend_tensor_get_f32(params["mix_factor"]);
float alpha = ggml_ext_backend_tensor_get_f32(params["mix_factor"]);
return sigmoid(alpha);
}

Expand Down
92 changes: 46 additions & 46 deletions conditioner.hpp
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Expand Up @@ -462,7 +462,7 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
clip_skip,
&chunk_hidden_states2, work_ctx);
// concat
chunk_hidden_states = ggml_tensor_concat(work_ctx, chunk_hidden_states1, chunk_hidden_states2, 0);
chunk_hidden_states = ggml_ext_tensor_concat(work_ctx, chunk_hidden_states1, chunk_hidden_states2, 0);

if (chunk_idx == 0) {
text_model2->compute(n_threads,
Expand All @@ -484,18 +484,18 @@ struct FrozenCLIPEmbedderWithCustomWords : public Conditioner {
LOG_DEBUG("computing condition graph completed, taking %" PRId64 " ms", t1 - t0);
ggml_tensor* result = ggml_dup_tensor(work_ctx, chunk_hidden_states);
{
float original_mean = ggml_tensor_mean(chunk_hidden_states);
float original_mean = ggml_ext_tensor_mean(chunk_hidden_states);
for (int i2 = 0; i2 < chunk_hidden_states->ne[2]; i2++) {
for (int i1 = 0; i1 < chunk_hidden_states->ne[1]; i1++) {
for (int i0 = 0; i0 < chunk_hidden_states->ne[0]; i0++) {
float value = ggml_tensor_get_f32(chunk_hidden_states, i0, i1, i2);
float value = ggml_ext_tensor_get_f32(chunk_hidden_states, i0, i1, i2);
value *= chunk_weights[i1];
ggml_tensor_set_f32(result, value, i0, i1, i2);
ggml_ext_tensor_set_f32(result, value, i0, i1, i2);
}
}
}
float new_mean = ggml_tensor_mean(result);
ggml_tensor_scale(result, (original_mean / new_mean));
float new_mean = ggml_ext_tensor_mean(result);
ggml_ext_tensor_scale_inplace(result, (original_mean / new_mean));
}
if (zero_out_masked) {
float* vec = (float*)result->data;
Expand Down Expand Up @@ -874,18 +874,18 @@ struct SD3CLIPEmbedder : public Conditioner {
work_ctx);
{
auto tensor = chunk_hidden_states_l;
float original_mean = ggml_tensor_mean(tensor);
float original_mean = ggml_ext_tensor_mean(tensor);
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float value = ggml_tensor_get_f32(tensor, i0, i1, i2);
float value = ggml_ext_tensor_get_f32(tensor, i0, i1, i2);
value *= chunk_weights[i1];
ggml_tensor_set_f32(tensor, value, i0, i1, i2);
ggml_ext_tensor_set_f32(tensor, value, i0, i1, i2);
}
}
}
float new_mean = ggml_tensor_mean(tensor);
ggml_tensor_scale(tensor, (original_mean / new_mean));
float new_mean = ggml_ext_tensor_mean(tensor);
ggml_ext_tensor_scale_inplace(tensor, (original_mean / new_mean));
}

if (chunk_idx == 0) {
Expand Down Expand Up @@ -932,18 +932,18 @@ struct SD3CLIPEmbedder : public Conditioner {

{
auto tensor = chunk_hidden_states_g;
float original_mean = ggml_tensor_mean(tensor);
float original_mean = ggml_ext_tensor_mean(tensor);
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float value = ggml_tensor_get_f32(tensor, i0, i1, i2);
float value = ggml_ext_tensor_get_f32(tensor, i0, i1, i2);
value *= chunk_weights[i1];
ggml_tensor_set_f32(tensor, value, i0, i1, i2);
ggml_ext_tensor_set_f32(tensor, value, i0, i1, i2);
}
}
}
float new_mean = ggml_tensor_mean(tensor);
ggml_tensor_scale(tensor, (original_mean / new_mean));
float new_mean = ggml_ext_tensor_mean(tensor);
ggml_ext_tensor_scale_inplace(tensor, (original_mean / new_mean));
}

if (chunk_idx == 0) {
Expand Down Expand Up @@ -984,18 +984,18 @@ struct SD3CLIPEmbedder : public Conditioner {
work_ctx);
{
auto tensor = chunk_hidden_states_t5;
float original_mean = ggml_tensor_mean(tensor);
float original_mean = ggml_ext_tensor_mean(tensor);
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float value = ggml_tensor_get_f32(tensor, i0, i1, i2);
float value = ggml_ext_tensor_get_f32(tensor, i0, i1, i2);
value *= chunk_weights[i1];
ggml_tensor_set_f32(tensor, value, i0, i1, i2);
ggml_ext_tensor_set_f32(tensor, value, i0, i1, i2);
}
}
}
float new_mean = ggml_tensor_mean(tensor);
ggml_tensor_scale(tensor, (original_mean / new_mean));
float new_mean = ggml_ext_tensor_mean(tensor);
ggml_ext_tensor_scale_inplace(tensor, (original_mean / new_mean));
}
} else {
chunk_hidden_states_t5 = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 4096, chunk_len);
Expand All @@ -1013,19 +1013,19 @@ struct SD3CLIPEmbedder : public Conditioner {
for (int i0 = 0; i0 < chunk_hidden_states_lg_pad->ne[0]; i0++) {
float value = 0.f;
if (i0 < chunk_hidden_states_l->ne[0]) {
value = ggml_tensor_get_f32(chunk_hidden_states_l, i0, i1, i2);
value = ggml_ext_tensor_get_f32(chunk_hidden_states_l, i0, i1, i2);
} else if (i0 < chunk_hidden_states_l->ne[0] + chunk_hidden_states_g->ne[0]) {
value = ggml_tensor_get_f32(chunk_hidden_states_g, i0 - chunk_hidden_states_l->ne[0], i1, i2);
value = ggml_ext_tensor_get_f32(chunk_hidden_states_g, i0 - chunk_hidden_states_l->ne[0], i1, i2);
}
ggml_tensor_set_f32(chunk_hidden_states_lg_pad, value, i0, i1, i2);
ggml_ext_tensor_set_f32(chunk_hidden_states_lg_pad, value, i0, i1, i2);
}
}
}

chunk_hidden_states = ggml_tensor_concat(work_ctx, chunk_hidden_states_lg_pad, chunk_hidden_states_t5, 1); // [n_token*2, 4096]
chunk_hidden_states = ggml_ext_tensor_concat(work_ctx, chunk_hidden_states_lg_pad, chunk_hidden_states_t5, 1); // [n_token*2, 4096]

if (chunk_idx == 0) {
pooled = ggml_tensor_concat(work_ctx, pooled_l, pooled_g, 0); // [768 + 1280]
pooled = ggml_ext_tensor_concat(work_ctx, pooled_l, pooled_g, 0); // [768 + 1280]
}

int64_t t1 = ggml_time_ms();
Expand Down Expand Up @@ -1269,18 +1269,18 @@ struct FluxCLIPEmbedder : public Conditioner {
work_ctx);
{
auto tensor = chunk_hidden_states;
float original_mean = ggml_tensor_mean(tensor);
float original_mean = ggml_ext_tensor_mean(tensor);
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float value = ggml_tensor_get_f32(tensor, i0, i1, i2);
float value = ggml_ext_tensor_get_f32(tensor, i0, i1, i2);
value *= chunk_weights[i1];
ggml_tensor_set_f32(tensor, value, i0, i1, i2);
ggml_ext_tensor_set_f32(tensor, value, i0, i1, i2);
}
}
}
float new_mean = ggml_tensor_mean(tensor);
ggml_tensor_scale(tensor, (original_mean / new_mean));
float new_mean = ggml_ext_tensor_mean(tensor);
ggml_ext_tensor_scale_inplace(tensor, (original_mean / new_mean));
}
} else {
chunk_hidden_states = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, 4096, chunk_len);
Expand Down Expand Up @@ -1483,18 +1483,18 @@ struct T5CLIPEmbedder : public Conditioner {
work_ctx);
{
auto tensor = chunk_hidden_states;
float original_mean = ggml_tensor_mean(tensor);
float original_mean = ggml_ext_tensor_mean(tensor);
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float value = ggml_tensor_get_f32(tensor, i0, i1, i2);
float value = ggml_ext_tensor_get_f32(tensor, i0, i1, i2);
value *= chunk_weights[i1];
ggml_tensor_set_f32(tensor, value, i0, i1, i2);
ggml_ext_tensor_set_f32(tensor, value, i0, i1, i2);
}
}
}
float new_mean = ggml_tensor_mean(tensor);
ggml_tensor_scale(tensor, (original_mean / new_mean));
float new_mean = ggml_ext_tensor_mean(tensor);
ggml_ext_tensor_scale_inplace(tensor, (original_mean / new_mean));
}

int64_t t1 = ggml_time_ms();
Expand All @@ -1505,7 +1505,7 @@ struct T5CLIPEmbedder : public Conditioner {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
if (chunk_mask[i1] < 0.f) {
ggml_tensor_set_f32(tensor, 0.f, i0, i1, i2);
ggml_ext_tensor_set_f32(tensor, 0.f, i0, i1, i2);
}
}
}
Expand Down Expand Up @@ -1664,7 +1664,7 @@ struct Qwen2_5_VLCLIPEmbedder : public Conditioner {
image.data = nullptr;

ggml_tensor* image_tensor = ggml_new_tensor_4d(work_ctx, GGML_TYPE_F32, resized_image.width, resized_image.height, 3, 1);
sd_image_f32_to_tensor(resized_image, image_tensor, false);
sd_image_f32_to_ggml_tensor(resized_image, image_tensor, false);
free(resized_image.data);
resized_image.data = nullptr;

Expand Down Expand Up @@ -1709,18 +1709,18 @@ struct Qwen2_5_VLCLIPEmbedder : public Conditioner {
work_ctx);
{
auto tensor = hidden_states;
float original_mean = ggml_tensor_mean(tensor);
float original_mean = ggml_ext_tensor_mean(tensor);
for (int i2 = 0; i2 < tensor->ne[2]; i2++) {
for (int i1 = 0; i1 < tensor->ne[1]; i1++) {
for (int i0 = 0; i0 < tensor->ne[0]; i0++) {
float value = ggml_tensor_get_f32(tensor, i0, i1, i2);
float value = ggml_ext_tensor_get_f32(tensor, i0, i1, i2);
value *= weights[i1];
ggml_tensor_set_f32(tensor, value, i0, i1, i2);
ggml_ext_tensor_set_f32(tensor, value, i0, i1, i2);
}
}
}
float new_mean = ggml_tensor_mean(tensor);
ggml_tensor_scale(tensor, (original_mean / new_mean));
float new_mean = ggml_ext_tensor_mean(tensor);
ggml_ext_tensor_scale_inplace(tensor, (original_mean / new_mean));
}

GGML_ASSERT(hidden_states->ne[1] > prompt_template_encode_start_idx);
Expand All @@ -1731,9 +1731,9 @@ struct Qwen2_5_VLCLIPEmbedder : public Conditioner {
hidden_states->ne[1] - prompt_template_encode_start_idx,
hidden_states->ne[2]);

ggml_tensor_iter(new_hidden_states, [&](ggml_tensor* new_hidden_states, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
float value = ggml_tensor_get_f32(hidden_states, i0, i1 + prompt_template_encode_start_idx, i2, i3);
ggml_tensor_set_f32(new_hidden_states, value, i0, i1, i2, i3);
ggml_ext_tensor_iter(new_hidden_states, [&](ggml_tensor* new_hidden_states, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
float value = ggml_ext_tensor_get_f32(hidden_states, i0, i1 + prompt_template_encode_start_idx, i2, i3);
ggml_ext_tensor_set_f32(new_hidden_states, value, i0, i1, i2, i3);
});

int64_t t1 = ggml_time_ms();
Expand Down
2 changes: 1 addition & 1 deletion control.hpp
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Expand Up @@ -230,7 +230,7 @@ class ControlNetBlock : public GGMLBlock {

auto middle_block_out = std::dynamic_pointer_cast<Conv2d>(blocks["middle_block_out.0"]);

auto t_emb = ggml_nn_timestep_embedding(ctx, timesteps, model_channels); // [N, model_channels]
auto t_emb = ggml_ext_timestep_embedding(ctx, timesteps, model_channels); // [N, model_channels]

auto emb = time_embed_0->forward(ctx, t_emb);
emb = ggml_silu_inplace(ctx, emb);
Expand Down
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