DarthAffe/stable-diffusion.cpp
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more progress; now alloc buffer failed

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bssrdf committed 2024-02-10 22:53:54 -05:00
1 parent d93951456d
commit 807c340118
5 files changed
+52 -12
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clip.hpp
+25 -6
View File
@@ -965,11 +965,15 @@ struct CLIPVisionModel {
struct ggml_tensor * inp = ggml_conv_2d(ctx0, patch_embeddings, x, patch_size, patch_size, 0, 0, 1, 1);
ggml_set_name(inp, "inp_conv_2d");
print_ggml_tensor(inp, true, "inp_conv_2d");
inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size);
ggml_set_name(inp, "inp_reshape_3d");
print_ggml_tensor(inp, true, "inp_reshape_3d");
// inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));
inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 2, 0, 3, 1));
print_ggml_tensor(ggml_permute(ctx0, inp, 2, 0, 1, 3), true, "inp_permute");
inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 2, 0, 1, 3));
print_ggml_tensor(inp, true, "inp_cont");
ggml_set_name(inp, "inp_cont");
ggml_set_name(class_embedding, "class_embedding");
@@ -977,23 +981,36 @@ struct CLIPVisionModel {
// struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size);
// struct ggml_tensor * temp = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, 1, batch_size);
// ggml_tensor *class_embedding_rep = ggml_add(ctx0, cast_f32_class, class_embedding);
// ggml_set_name(class_embedding_rep, "add_class_embedding_to_zero");
print_ggml_tensor(class_embedding, true, "model.class_embedding");
// print_ggml_tensor(class_embedding_rep, true, "class_embedding_rep_bef_repeat");
print_ggml_tensor(temp, true, "temp");
ggml_tensor *class_embedding_rep = ggml_repeat(ctx0, class_embedding, temp);
ggml_set_name(class_embedding_rep, "class_embedding_rep");
ggml_set_name(class_embedding_rep, "class_embedding_rep");
print_ggml_tensor(class_embedding_rep, true, "class_embedding_rep");
class_embedding_rep = ggml_cast(ctx0, class_embedding_rep, inp->type);
print_ggml_tensor(class_embedding_rep, true, "class_embedding_rep_aft_casting");
struct ggml_tensor *embeddings = ggml_concat(ctx0, class_embedding_rep, inp);
ggml_set_name(embeddings, "embeddings_after_concat");
embeddings = ggml_cont(ctx0, ggml_permute(ctx0, embeddings, 0, 3, 1, 2));
print_ggml_tensor(embeddings, true, "embeddings_after_concat");
// print_ggml_tensor(ggml_permute(ctx0, embeddings, 0, 3, 1, 2), true, "embeddings_after_concat_permute");
embeddings = ggml_cont(ctx0, ggml_permute(ctx0, embeddings, 0, 2, 1, 3));
ggml_set_name(embeddings, "embeddings_after_permute");
// embeddings = ggml_acc(ctx0, embeddings, ggml_repeat(ctx0, class_embedding, temp), embeddings->nb[1],
// embeddings->nb[2], embeddings->nb[3], 0);
// struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);
// print_ggml_tensor(embeddings, true, "embeddings_bef_add");
print_ggml_tensor(ggml_get_rows(ctx0, position_embeddings, positions), true, "embeddings_add_src1");
// print_ggml_tensor(ggml_repeat(ctx0, ggml_get_rows(ctx0, position_embeddings, positions), embeddings), true, "embeddings_add_src1");
// embeddings = ggml_cont(ctx0, ggml_permute(ctx0, embeddings, 0, 1, 3, 2));
embeddings =
ggml_add(ctx0, embeddings, ggml_repeat(ctx0, ggml_get_rows(ctx0, position_embeddings, positions), embeddings));
ggml_set_name(embeddings, "embeddings_after_add");
print_ggml_tensor(embeddings, true, "embeddings_after_add");
// pre-layernorm
embeddings = ggml_nn_layer_norm(ctx0, embeddings, pre_ln_w, pre_ln_w);
@@ -1023,6 +1040,7 @@ struct CLIPVisionModel {
}
void init_params(ggml_context* ctx, ggml_backend_t backend, ggml_type wtype, ggml_allocr* alloc) {
wtype = GGML_TYPE_F32;
class_embedding = ggml_new_tensor_1d(ctx, wtype, hidden_size);
patch_embeddings = ggml_new_tensor_4d(ctx, wtype, patch_size, patch_size, 3, hidden_size);
@@ -1142,7 +1160,8 @@ struct FrozenCLIPEmbedderWithCustomWords : public GGMLModule {
hidden_states2->ne[2],
hidden_states2->ne[3]);
hidden_states2 = ggml_cont(ctx0, ggml_permute(ctx0, hidden_states2, 2, 0, 1, 3));
// print_ggml_tensor(hidden_states, true, "hidden_states");
// print_ggml_tensor(hidden_states2, true, "hidden_states2");
hidden_states = ggml_concat(ctx0, hidden_states, hidden_states2); // [N, n_token, hidden_size + hidden_size2]
hidden_states = ggml_cont(ctx0, ggml_permute(ctx0, hidden_states, 1, 2, 0, 3));
ggml_extend.hpp
+1 -1
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@@ -81,7 +81,7 @@ __STATIC_INLINE__ ggml_fp16_t ggml_tensor_get_f16(const ggml_tensor* tensor, int
__STATIC_INLINE__ void print_ggml_tensor(struct ggml_tensor* tensor, bool shape_only = false, const char *mark = "") {
printf("%s: shape(%zu, %zu, %zu, %zu)\n", mark, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
printf("%s (%s): shape(%zu, %zu, %zu, %zu)\n", mark, ggml_type_name(tensor->type), tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]);
fflush(stdout);
if (shape_only) {
return;
model.cpp
+3 -1
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@@ -789,6 +789,8 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const
ne[i] = shape[i].get<int64_t>();
}
TensorStorage tensor_storage(prefix + name, type, ne, n_dims, file_index, ST_HEADER_SIZE_LEN + header_size_ + begin);
// if(strcmp(prefix.c_str(), "pmid") == 0)
// printf(" %s %s \n", (prefix + name).c_str(), ggml_type_name(type));
tensor_storage.reverse_ne();
size_t tensor_data_size = end - begin;
@@ -1126,7 +1128,7 @@ bool ModelLoader::parse_data_pkl(uint8_t* buffer,
reader.tensor_storage.reverse_ne();
reader.tensor_storage.file_index = file_index;
// if(strcmp(prefix.c_str(), "pm") == 0)
printf(" got tensor %s \n ", reader.tensor_storage.name.c_str());
// printf(" got tensor %s \n ", reader.tensor_storage.name.c_str());
reader.tensor_storage.name = prefix + reader.tensor_storage.name;
tensor_storages.push_back(reader.tensor_storage);
// LOG_DEBUG("%s", reader.tensor_storage.name.c_str());
pmid.hpp
+16 -4
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@@ -37,7 +37,7 @@ struct FuseBlock {
size_t mem_size = 0;
mem_size += 2 * ggml_row_size(wtype, in_dim);
mem_size += ggml_row_size(wtype, in_dim*hidden_dim);
mem_size += 5 * ggml_row_size(wtype, in_dim);
mem_size += ggml_row_size(wtype, out_dim);
mem_size += ggml_row_size(wtype, hidden_dim*out_dim);
mem_size += ggml_row_size(wtype, hidden_dim);
@@ -281,10 +281,15 @@ struct PhotoMakerIDEncoder : public GGMLModule {
size_t calculate_mem_size() {
size_t mem_size = vision_model.calculate_mem_size(wtype);
LOG_INFO("pmid vision memory buffer size = %.2fMB ",
vision_model.calculate_mem_size(wtype) / 1024.0 / 1024.0);
mem_size += fuse_module.calculate_mem_size(wtype);
LOG_INFO("pmid fuse memory buffer size = %.2fMB ",
fuse_module.calculate_mem_size(wtype) / 1024.0 / 1024.0);
mem_size += ggml_row_size(wtype, 1280*1024);
LOG_INFO("pmid porject buffer size = %.2fMB ",
ggml_row_size(wtype, 1280*1024) / 1024.0 / 1024.0);
return mem_size;
}
@@ -393,7 +398,7 @@ struct PhotoMakerIDEncoder : public GGMLModule {
int64_t hidden_size = prompt_embeds->ne[0];
int64_t seq_length = prompt_embeds->ne[1];
ggml_type type = prompt_embeds->type;
ggml_type type = GGML_TYPE_F32;
struct ggml_tensor* id_pixel_values_d = ggml_dup_tensor(ctx0, id_pixel_values);
ggml_allocr_alloc(allocr, id_pixel_values_d);
@@ -441,11 +446,13 @@ struct PhotoMakerIDEncoder : public GGMLModule {
struct ggml_tensor * cls = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, batch_size);
struct ggml_tensor * class_embedding_temp = ggml_new_tensor_4d(ctx0, type,
vision_model.hidden_size, 1, 1, batch_size);
vision_model.hidden_size, batch_size, 1, 1);
struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions);
struct ggml_tensor * cast_f32_class = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, vision_model.hidden_size);
ggml_allocr_alloc(allocr, cls);
ggml_allocr_alloc(allocr, class_embedding_temp);
ggml_allocr_alloc(allocr, positions);
ggml_allocr_alloc(allocr, cast_f32_class);
@@ -467,6 +474,11 @@ struct PhotoMakerIDEncoder : public GGMLModule {
ggml_backend_tensor_set(cls, cls_h.data(), 0, ggml_nbytes(cls));
ggml_backend_tensor_set(positions, pos.data(), 0, ggml_nbytes(positions));
ggml_backend_tensor_set(class_tokens_mask_pos, ctmpos.data(), 0, ggml_nbytes(class_tokens_mask_pos));
std::vector<float> zeros;
for (int i = 0; i < hidden_size; i++) {
zeros.push_back(0.f);
}
ggml_backend_tensor_set(cast_f32_class, zeros.data(), 0, ggml_nbytes(cast_f32_class));
if(left){
if(type == GGML_TYPE_F16){
std::vector<ggml_fp16_t> zeros(ggml_nelements(left), ggml_fp32_to_fp16(0.f));
stable-diffusion.cpp
+7
View File
@@ -218,6 +218,8 @@ public:
LOG_ERROR(" pmid model params buffer allocation failed");
return false;
}
LOG_INFO("pmid param memory buffer size = %.2fMB ",
pmid_model.params_buffer_size / 1024.0 / 1024.0);
}
@@ -248,9 +250,12 @@ public:
first_stage_model.map_by_name(tensors, "first_stage_model.");
if(stacked_id){
printf("preparing memory for pmid \n");
pmid_model.init_params();
pmid_model.map_by_name(tensors, "pmid.");
printf("done preparing memory for pmid \n");
}
}
struct ggml_init_params params;
@@ -311,6 +316,8 @@ public:
cond_stage_model.params_buffer_size +
diffusion_model.params_buffer_size +
first_stage_model.params_buffer_size;
if(stacked_id)
total_params_size += pmid_model.params_buffer_size;
LOG_INFO("total memory buffer size = %.2fMB (clip %.2fMB, unet %.2fMB, vae %.2fMB)",
total_params_size / 1024.0 / 1024.0,
cond_stage_model.params_buffer_size / 1024.0 / 1024.0,