mirror of
https://github.com/leejet/stable-diffusion.cpp.git
synced 2026-11-11 19:36:33 +00:00
style: format code
This commit is contained in:
leejet committed
1 parent
36ec16ac99
commit
349439f239
14 files changed
+200
-202
No files matched your search
@@ -241,7 +241,7 @@ public:
|
||||
std::vector<int> tokenize(std::string text,
|
||||
on_new_token_cb_t on_new_token_cb,
|
||||
size_t max_length = 0,
|
||||
bool padding = false) {
|
||||
bool padding = false) {
|
||||
std::vector<int32_t> tokens = encode(text, on_new_token_cb);
|
||||
tokens.insert(tokens.begin(), BOS_TOKEN_ID);
|
||||
if (max_length > 0) {
|
||||
@@ -486,7 +486,6 @@ struct ResidualAttentionBlock {
|
||||
|
||||
ln2_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
|
||||
ln2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
|
||||
|
||||
}
|
||||
|
||||
void map_by_name(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
|
||||
@@ -661,8 +660,8 @@ struct CLIPTextModel {
|
||||
mem_size += ggml_row_size(GGML_TYPE_I32, hidden_size * max_position_embeddings); // position_ids
|
||||
mem_size += ggml_row_size(wtype, hidden_size * vocab_size); // token_embed_weight
|
||||
mem_size += ggml_row_size(wtype, hidden_size * max_position_embeddings); // position_embed_weight
|
||||
if(version == OPENAI_CLIP_VIT_L_14) {
|
||||
mem_size += ggml_row_size(wtype, hidden_size * max_position_embeddings); // token_embed_custom
|
||||
if (version == OPENAI_CLIP_VIT_L_14) {
|
||||
mem_size += ggml_row_size(wtype, hidden_size * max_position_embeddings); // token_embed_custom
|
||||
}
|
||||
for (int i = 0; i < num_hidden_layers; i++) {
|
||||
mem_size += resblocks[i].calculate_mem_size(wtype);
|
||||
@@ -688,32 +687,32 @@ struct CLIPTextModel {
|
||||
}
|
||||
}
|
||||
|
||||
bool load_embedding(std::string embd_name, std::string embd_path, std::vector<int32_t> &bpe_tokens) {
|
||||
bool load_embedding(std::string embd_name, std::string embd_path, std::vector<int32_t>& bpe_tokens) {
|
||||
// the order matters
|
||||
ModelLoader model_loader;
|
||||
if(!model_loader.init_from_file(embd_path)) {
|
||||
if (!model_loader.init_from_file(embd_path)) {
|
||||
LOG_ERROR("embedding '%s' failed", embd_name.c_str());
|
||||
return false;
|
||||
}
|
||||
struct ggml_init_params params;
|
||||
params.mem_size = 32 * 1024; // max for custom embeddings 32 KB
|
||||
params.mem_buffer = NULL;
|
||||
params.no_alloc = false;
|
||||
params.mem_size = 32 * 1024; // max for custom embeddings 32 KB
|
||||
params.mem_buffer = NULL;
|
||||
params.no_alloc = false;
|
||||
struct ggml_context* embd_ctx = ggml_init(params);
|
||||
struct ggml_tensor* embd = NULL;
|
||||
auto on_load = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) {
|
||||
if(tensor_storage.ne[0] != hidden_size) {
|
||||
struct ggml_tensor* embd = NULL;
|
||||
auto on_load = [&](const TensorStorage& tensor_storage, ggml_tensor** dst_tensor) {
|
||||
if (tensor_storage.ne[0] != hidden_size) {
|
||||
LOG_DEBUG("embedding wrong hidden size, got %i, expected %i", tensor_storage.ne[0], hidden_size);
|
||||
return false;
|
||||
}
|
||||
embd = ggml_new_tensor_2d(embd_ctx, token_embed_weight->type, hidden_size, tensor_storage.n_dims > 1 ? tensor_storage.ne[1] : 1);
|
||||
embd = ggml_new_tensor_2d(embd_ctx, token_embed_weight->type, hidden_size, tensor_storage.n_dims > 1 ? tensor_storage.ne[1] : 1);
|
||||
*dst_tensor = embd;
|
||||
return true;
|
||||
};
|
||||
model_loader.load_tensors(on_load, NULL);
|
||||
ggml_backend_tensor_set(token_embed_custom, embd->data, num_custom_embeddings * hidden_size * ggml_type_size(token_embed_custom->type), ggml_nbytes(embd));
|
||||
readed_embeddings.push_back(embd_name);
|
||||
for(int i = 0; i < embd->ne[1]; i++) {
|
||||
for (int i = 0; i < embd->ne[1]; i++) {
|
||||
bpe_tokens.push_back(vocab_size + num_custom_embeddings);
|
||||
// LOG_DEBUG("new custom token: %i", vocab_size + num_custom_embeddings);
|
||||
num_custom_embeddings++;
|
||||
@@ -775,7 +774,7 @@ struct CLIPTextModel {
|
||||
|
||||
final_ln_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
|
||||
|
||||
if(version == OPENAI_CLIP_VIT_L_14) {
|
||||
if (version == OPENAI_CLIP_VIT_L_14) {
|
||||
token_embed_custom = ggml_new_tensor_2d(ctx, wtype, hidden_size, max_position_embeddings);
|
||||
}
|
||||
|
||||
@@ -878,11 +877,11 @@ struct FrozenCLIPEmbedderWithCustomWords : public GGMLModule {
|
||||
|
||||
auto hidden_states2 = text_model2.forward(ctx0, input_ids2, NULL); // [N, n_token, hidden_size2]
|
||||
hidden_states2 = ggml_reshape_4d(ctx0,
|
||||
hidden_states2,
|
||||
hidden_states2->ne[0],
|
||||
hidden_states2->ne[1],
|
||||
hidden_states2->ne[2],
|
||||
hidden_states2->ne[3]);
|
||||
hidden_states2,
|
||||
hidden_states2->ne[0],
|
||||
hidden_states2->ne[1],
|
||||
hidden_states2->ne[2],
|
||||
hidden_states2->ne[3]);
|
||||
hidden_states2 = ggml_cont(ctx0, ggml_permute(ctx0, hidden_states2, 2, 0, 1, 3));
|
||||
|
||||
hidden_states = ggml_concat(ctx0, hidden_states, hidden_states2); // [N, n_token, hidden_size + hidden_size2]
|
||||
@@ -913,20 +912,20 @@ struct FrozenCLIPEmbedderWithCustomWords : public GGMLModule {
|
||||
LOG_DEBUG("parse '%s' to %s", text.c_str(), ss.str().c_str());
|
||||
}
|
||||
|
||||
auto on_new_token_cb = [&] (std::string& str, std::vector<int32_t> &bpe_tokens) -> bool {
|
||||
size_t word_end = str.find(",");
|
||||
auto on_new_token_cb = [&](std::string& str, std::vector<int32_t>& bpe_tokens) -> bool {
|
||||
size_t word_end = str.find(",");
|
||||
std::string embd_name = word_end == std::string::npos ? str : str.substr(0, word_end);
|
||||
embd_name = trim(embd_name);
|
||||
embd_name = trim(embd_name);
|
||||
std::string embd_path = get_full_path(text_model.embd_dir, embd_name + ".pt");
|
||||
if(embd_path.size() == 0) {
|
||||
if (embd_path.size() == 0) {
|
||||
embd_path = get_full_path(text_model.embd_dir, embd_name + ".ckpt");
|
||||
}
|
||||
if(embd_path.size() == 0) {
|
||||
if (embd_path.size() == 0) {
|
||||
embd_path = get_full_path(text_model.embd_dir, embd_name + ".safetensors");
|
||||
}
|
||||
if(embd_path.size() > 0) {
|
||||
if(text_model.load_embedding(embd_name, embd_path, bpe_tokens)) {
|
||||
if(word_end != std::string::npos) {
|
||||
if (embd_path.size() > 0) {
|
||||
if (text_model.load_embedding(embd_name, embd_path, bpe_tokens)) {
|
||||
if (word_end != std::string::npos) {
|
||||
str = str.substr(word_end);
|
||||
} else {
|
||||
str = "";
|
||||
@@ -1033,7 +1032,7 @@ struct FrozenCLIPEmbedderWithCustomWords : public GGMLModule {
|
||||
|
||||
struct ggml_tensor* embeddings = NULL;
|
||||
|
||||
if(text_model.num_custom_embeddings > 0 && version != VERSION_XL) {
|
||||
if (text_model.num_custom_embeddings > 0 && version != VERSION_XL) {
|
||||
embeddings = ggml_new_tensor_2d(ctx0, wtype, text_model.hidden_size, text_model.vocab_size + text_model.num_custom_embeddings /* custom placeholder */);
|
||||
ggml_allocr_alloc(allocr, embeddings);
|
||||
if (!ggml_allocr_is_measure(allocr)) {
|
||||
|
||||
Reference in new issue
Block a user