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fixed wtype issue; input images can only be 1 because issue with transformer when batch size > 1 (to be investigated)

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bssrdf
2024-02-11 12:54:18 -05:00
parent 807c340118
commit 5a13b48b6e
4 changed files with 83 additions and 91 deletions
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57
clip.hpp
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@@ -908,7 +908,9 @@ struct CLIPVisionModel {
}
size_t calculate_mem_size(ggml_type wtype) {
size_t mem_size = 0;
size_t mem_size = 0;
for (int i = 0; i < num_hidden_layers; i++) {
mem_size += resblocks[i].calculate_mem_size(wtype);
}
@@ -917,7 +919,6 @@ struct CLIPVisionModel {
mem_size += ggml_row_size(wtype, hidden_size*patch_size*patch_size*3); //
mem_size += ggml_row_size(wtype, hidden_size*257); //
mem_size += ggml_row_size(wtype, hidden_size * projection_dim); // visual_projection
return mem_size;
}
@@ -961,19 +962,23 @@ struct CLIPVisionModel {
ggml_set_name(x, "id_pixel");
ggml_set_name(temp, "temp_input");
int64_t* ne = patch_embeddings->ne;
struct ggml_tensor *patch_embeddings_f16 = ggml_reshape_3d(ctx0, patch_embeddings, ne[0], ne[1], ne[2]*ne[3]);
patch_embeddings_f16 = ggml_cast(ctx0, patch_embeddings_f16, GGML_TYPE_F16);
patch_embeddings_f16 = ggml_reshape_4d(ctx0, patch_embeddings_f16, ne[0], ne[1], ne[2], ne[3]);
struct ggml_tensor * inp = ggml_conv_2d(ctx0, patch_embeddings, x, patch_size, patch_size, 0, 0, 1, 1);
struct ggml_tensor * inp = ggml_conv_2d(ctx0, patch_embeddings_f16, x, patch_size, patch_size, 0, 0, 1, 1);
ggml_set_name(inp, "inp_conv_2d");
print_ggml_tensor(inp, true, "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");
// print_ggml_tensor(inp, true, "inp_reshape_3d");
// inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3));
print_ggml_tensor(ggml_permute(ctx0, inp, 2, 0, 1, 3), true, "inp_permute");
// 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");
// print_ggml_tensor(inp, true, "inp_cont");
ggml_set_name(inp, "inp_cont");
ggml_set_name(class_embedding, "class_embedding");
@@ -983,18 +988,18 @@ struct CLIPVisionModel {
// 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, true, "model.class_embedding");
// print_ggml_tensor(class_embedding_rep, true, "class_embedding_rep_bef_repeat");
print_ggml_tensor(temp, true, "temp");
// 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");
print_ggml_tensor(class_embedding_rep, true, "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");
// 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");
print_ggml_tensor(embeddings, true, "embeddings_after_concat");
// 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");
@@ -1004,13 +1009,13 @@ struct CLIPVisionModel {
// 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_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");
// print_ggml_tensor(embeddings, true, "embeddings_after_add");
// pre-layernorm
embeddings = ggml_nn_layer_norm(ctx0, embeddings, pre_ln_w, pre_ln_w);
@@ -1040,25 +1045,33 @@ 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);
position_embeddings = ggml_new_tensor_2d(ctx, wtype, hidden_size, 257);
for (int i = 0; i < num_hidden_layers; i++) {
resblocks[i].init_params(ctx, alloc, wtype);
}
class_embedding = ggml_new_tensor_1d(ctx, wtype, hidden_size);
ggml_set_name(class_embedding, "class_embedding");
patch_embeddings = ggml_new_tensor_4d(ctx, wtype, patch_size, patch_size, 3, hidden_size);
ggml_set_name(patch_embeddings, "patch_embeddings");
position_embeddings = ggml_new_tensor_2d(ctx, wtype, hidden_size, 257);
ggml_set_name(position_embeddings, "position_embeddings");
pre_ln_w = ggml_new_tensor_1d(ctx, wtype, hidden_size);
ggml_set_name(pre_ln_w, "vision.pre_ln_w");
pre_ln_b = ggml_new_tensor_1d(ctx, wtype, hidden_size);
ggml_set_name(pre_ln_b, "vision.pre_ln_b");
post_ln_w = ggml_new_tensor_1d(ctx, wtype, hidden_size);
ggml_set_name(post_ln_w, "vision.post_ln_w");
post_ln_b = ggml_new_tensor_1d(ctx, wtype, hidden_size);
ggml_set_name(post_ln_b, "vision.post_ln_b");
visual_projection = ggml_new_tensor_2d(ctx, wtype, hidden_size, projection_dim); // Check!!!
ggml_set_name(visual_projection, "vision.visual_projection");
// alloc all tensors linked to this context
for (struct ggml_tensor* t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (t->data == NULL) {

9
ggml_extend.hpp
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@@ -651,19 +651,18 @@ struct GGMLModule {
ggml_backend_metal_set_n_cb(backend, n_threads);
}
#endif
printf("about to do ggml_backend_graph_compute \n");
ggml_backend_graph_compute(backend, gf);
#ifdef GGML_PERF
ggml_graph_print(gf);
#endif
if (output != NULL)
print_ggml_tensor(output, true, "output_before_get");
// if (output != NULL)
// print_ggml_tensor(output, true, "output_before_get");
if (output != NULL) {
ggml_backend_tensor_get_and_sync(backend, gf->nodes[gf->n_nodes - 1], output->data, 0, ggml_nbytes(output));
}
if (output != NULL)
print_ggml_tensor(output, true, "output_get");
// if (output != NULL)
// print_ggml_tensor(output, true, "output_get");
}
void free_compute_buffer() {

70
pmid.hpp
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@@ -204,15 +204,15 @@ struct FuseModule{
struct ggml_tensor * valid_id_embeds = id_embeds;
// # slice out the image token embeddings
struct ggml_tensor * image_token_embeds = ggml_get_rows(ctx, prompt_embeds, class_tokens_mask_pos);
print_ggml_tensor(image_token_embeds, true, "image_token_embeds");
// print_ggml_tensor(image_token_embeds, true, "image_token_embeds");
struct ggml_tensor *stacked_id_embeds = fuse_fn(ctx, image_token_embeds, valid_id_embeds);
print_ggml_tensor(stacked_id_embeds, true, "stacked_id_embeds_before_concat");
// print_ggml_tensor(stacked_id_embeds, true, "stacked_id_embeds_before_concat");
stacked_id_embeds = ggml_cont(ctx, ggml_permute(ctx, stacked_id_embeds, 0, 2, 1, 3));
print_ggml_tensor(stacked_id_embeds, true, "stacked_id_embeds_after_permute");
// print_ggml_tensor(stacked_id_embeds, true, "stacked_id_embeds_after_permute");
if(left && right){
print_ggml_tensor(left, true, "left");
print_ggml_tensor(right, true, "right");
// print_ggml_tensor(left, true, "left");
// print_ggml_tensor(right, true, "right");
stacked_id_embeds = ggml_concat(ctx, left, stacked_id_embeds);
stacked_id_embeds = ggml_concat(ctx, stacked_id_embeds, right);
}else if(left){
@@ -220,24 +220,24 @@ struct FuseModule{
}else if(right){
stacked_id_embeds = ggml_concat(ctx, stacked_id_embeds, right);
}
print_ggml_tensor(stacked_id_embeds, true, "stacked_id_embeds_after_concat");
// print_ggml_tensor(stacked_id_embeds, true, "stacked_id_embeds_after_concat");
stacked_id_embeds = ggml_cont(ctx, ggml_permute(ctx, stacked_id_embeds, 0, 2, 1, 3));
print_ggml_tensor(stacked_id_embeds, true, "stacked_id_embeds_after_permute_2");
print_ggml_tensor(class_tokens_mask, true, "class_tokens_mask");
print_ggml_tensor(prompt_embeds, true, "prompt_embeds");
// print_ggml_tensor(stacked_id_embeds, true, "stacked_id_embeds_after_permute_2");
// print_ggml_tensor(class_tokens_mask, true, "class_tokens_mask");
// print_ggml_tensor(prompt_embeds, true, "prompt_embeds");
// assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}"
// prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype))
// struct ggml_tensor *prompt_embeds_perm = ggml_cont(ctx, ggml_permute(ctx, prompt_embeds, 1, 0, 2, 3));
struct ggml_tensor *prompt_embeds_perm = ggml_cont(ctx, ggml_transpose(ctx, prompt_embeds));
print_ggml_tensor(prompt_embeds_perm, true, "prompt_embeds_perm");
// print_ggml_tensor(prompt_embeds_perm, true, "prompt_embeds_perm");
class_tokens_mask = ggml_repeat(ctx, class_tokens_mask, prompt_embeds_perm);
print_ggml_tensor(class_tokens_mask, true, "class_tokens_mask_repeat");
// print_ggml_tensor(class_tokens_mask, true, "class_tokens_mask_repeat");
class_tokens_mask = ggml_cont(ctx, ggml_transpose(ctx, class_tokens_mask));
print_ggml_tensor(class_tokens_mask, true, "class_tokens_mask_transpose");
// print_ggml_tensor(class_tokens_mask, true, "class_tokens_mask_transpose");
prompt_embeds = ggml_mul(ctx, prompt_embeds, class_tokens_mask);
print_ggml_tensor(prompt_embeds, true, "prompt_embeds_after_mul");
// print_ggml_tensor(prompt_embeds, true, "prompt_embeds_after_mul");
struct ggml_tensor * updated_prompt_embeds = ggml_add(ctx, prompt_embeds, stacked_id_embeds);
print_ggml_tensor(updated_prompt_embeds, true, "updated_prompt_embeds");
// print_ggml_tensor(updated_prompt_embeds, true, "updated_prompt_embeds");
// updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1)
return updated_prompt_embeds;
}
@@ -257,18 +257,18 @@ struct PhotoMakerIDEncoder : public GGMLModule {
fuse_module(2048) {
vision_model = CLIPVisionModel();
// fuse_module = FuseModule(2048);
// wtype = GGML_TYPE_F32;
}
// void init_params(ggml_context* ctx, ggml_backend_t backend, ggml_type wtype, ggml_allocr* alloc) {
void init_params() {
void init_params(ggml_type wtype) {
// LOG_INFO(" PMID wtype: %s", ggml_type_name(wtype));
ggml_allocr* alloc = ggml_allocr_new_from_buffer(params_buffer);
vision_model.init_params(params_ctx, backend, wtype, alloc);
fuse_module.init_params(params_ctx, wtype, alloc);
// visual_projection_2 = ggml_new_tensor_2d(params_ctx, wtype, 1280, 1024); // python [1024, 1280]
visual_projection_2 = ggml_new_tensor_2d(params_ctx, wtype, 1024, 1280); // python [1024, 1280]
visual_projection_2 = ggml_new_tensor_2d(params_ctx, wtype, 1024, 1280); // python [1024, 1280]
ggml_allocr_alloc(alloc, visual_projection_2);
ggml_allocr_free(alloc);
}
@@ -279,17 +279,15 @@ struct PhotoMakerIDEncoder : public GGMLModule {
tensors[prefix + "visual_projection_2.weight"] = visual_projection_2;
}
size_t calculate_mem_size() {
size_t calculate_mem_size() {
wtype = GGML_TYPE_F32;
// LOG_INFO(" PMID wtype: %s", ggml_type_name(wtype));
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;
}
@@ -325,15 +323,15 @@ struct PhotoMakerIDEncoder : public GGMLModule {
ggml_set_name(right, "right_input");
print_ggml_tensor(prompt_embeds, true, "prompt_embeds");
print_ggml_tensor(class_embedding_temp, true, "class_embedding_temp");
// print_ggml_tensor(prompt_embeds, true, "prompt_embeds");
// print_ggml_tensor(class_embedding_temp, true, "class_embedding_temp");
struct ggml_tensor *shared_id_embeds = vision_model.forward(ctx,
id_pixel_values,
cls,
class_embedding_temp,
positions
); // [batch_size, seq_length, hidden_size]
print_ggml_tensor(shared_id_embeds, true, "shared_id_embeds");
// print_ggml_tensor(shared_id_embeds, true, "shared_id_embeds");
// if(class_tokens_mask->backend == GGML_BACKEND_GPU){
// int *ctm = (int *)malloc(class_tokens_mask->ne[0]);
@@ -350,20 +348,20 @@ struct PhotoMakerIDEncoder : public GGMLModule {
// id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1)
// id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1)
print_ggml_tensor(id_embeds, true, "id_embeds");
print_ggml_tensor(id_embeds_2, true, "id_embeds_2");
// print_ggml_tensor(id_embeds, true, "id_embeds");
// print_ggml_tensor(id_embeds_2, true, "id_embeds_2");
id_embeds = ggml_cont(ctx, ggml_permute(ctx, id_embeds, 2, 0, 1, 3));
id_embeds_2 = ggml_cont(ctx, ggml_permute(ctx, id_embeds_2, 2, 0, 1, 3));
print_ggml_tensor(id_embeds, true, "id_embeds_after_perm");
print_ggml_tensor(id_embeds_2, true, "id_embeds_2_after_perm");
// print_ggml_tensor(id_embeds, true, "id_embeds_after_perm");
// print_ggml_tensor(id_embeds_2, true, "id_embeds_2_after_perm");
id_embeds = ggml_concat(ctx, id_embeds, id_embeds_2); // [batch_size, seq_length, 1, 2048] check whether concat at dim 2 is right
print_ggml_tensor(id_embeds, true, "id_embeds_after_cat");
// print_ggml_tensor(id_embeds, true, "id_embeds_after_cat");
id_embeds = ggml_cont(ctx, ggml_permute(ctx, id_embeds, 1, 2, 0, 3));
print_ggml_tensor(id_embeds, true, "id_embeds_after_cat+perm");
// print_ggml_tensor(id_embeds, true, "id_embeds_after_cat+perm");
struct ggml_tensor * updated_prompt_embeds = fuse_module.forward(ctx,
@@ -371,7 +369,7 @@ struct PhotoMakerIDEncoder : public GGMLModule {
class_tokens_mask,
class_tokens_mask_pos,
left, right);
print_ggml_tensor(updated_prompt_embeds, true, "updated_prompt_embeds_returned");
// print_ggml_tensor(updated_prompt_embeds, true, "updated_prompt_embeds_returned");
return updated_prompt_embeds;
@@ -508,7 +506,7 @@ struct PhotoMakerIDEncoder : public GGMLModule {
positions,
left, right
);
print_ggml_tensor(updated_prompt_embeds, true, "updated_prompt_embeds_returned_forward");
// print_ggml_tensor(updated_prompt_embeds, true, "updated_prompt_embeds_returned_forward");
ggml_build_forward_expand(gf, updated_prompt_embeds);
// ggml_graph_dump_dot(gf, NULL, "id_encoder.dot");
ggml_free(ctx0);

38
stable-diffusion.cpp
View File

@@ -250,10 +250,10 @@ public:
first_stage_model.map_by_name(tensors, "first_stage_model.");
if(stacked_id){
printf("preparing memory for pmid \n");
pmid_model.init_params();
// printf("preparing memory for pmid \n");
pmid_model.init_params(GGML_TYPE_F32);
// printf("done preparing memory for pmid \n");
pmid_model.map_by_name(tensors, "pmid.");
printf("done preparing memory for pmid \n");
}
}
@@ -506,10 +506,10 @@ public:
bool force_zero_embeddings = false) {
cond_stage_model.set_clip_skip(clip_skip);
auto image_tokens = cond_stage_model.convert_token_to_id(trigger_word);
printf(" length of image tokens: %lu \n", image_tokens.size());
if(image_tokens.size() == 1){
printf(" image token id is: %d \n", image_tokens[0]);
}
// printf(" length of image tokens: %lu \n", image_tokens.size());
// if(image_tokens.size() == 1){
// printf(" image token id is: %d \n", image_tokens[0]);
// }
GGML_ASSERT(image_tokens.size() == 1);
// auto tokens_and_weights = cond_stage_model.tokenize(text, true);
auto tokens_and_weights = cond_stage_model.tokenize_with_trigger_token(text,
@@ -1475,25 +1475,13 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
float std[] = {0.26862954, 0.26130258, 0.27577711};
for(int i = 0; i < num_input_images; i++) {
sd_image_t* init_image = input_id_images[i];
sd_mul_images_to_tensor(init_image->data, init_img, i, mean, std);
}
int64_t *ne = init_img->ne;
fprintf(stderr, "%s: input id image tensor ne [%ld, %ld, %ld, %ld] \n",
__func__, ne[0], ne[1], ne[2], ne[3]);
sd_mul_images_to_tensor(init_image->data, init_img, i, mean, std);
}
auto cond_tup = sd_ctx->sd->get_learned_condition_with_trigger(work_ctx, prompt,
clip_skip, width, height, num_input_images );
prompts_embeds = std::get<0>(cond_tup);
pooled_prompts_embeds = std::get<1>(cond_tup); // [adm_in_channels, ]
class_tokens_mask = std::get<2>(cond_tup); //
ne = prompts_embeds->ne;
fprintf(stderr, "%s: text embedding tensor ne [%ld, %ld, %ld, %ld] \n",
__func__, ne[0], ne[1], ne[2], ne[3]);
ne = pooled_prompts_embeds->ne;
fprintf(stderr, "%s: SDXL pooled text embedding tensor ne [%ld, %ld, %ld, %ld] \n",
__func__, ne[0], ne[1], ne[2], ne[3]);
// ne = class_tokens_mask->ne;
// fprintf(stderr, "%s: class token mask tensor ne [%ld, %ld, %ld, %ld] \n",
// __func__, ne[0], ne[1], ne[2], ne[3]);
prompts_embeds = sd_ctx->sd->id_encoder(work_ctx, init_img, prompts_embeds, class_tokens_mask);
@@ -1512,13 +1500,7 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
auto cond_pair = sd_ctx->sd->get_learned_condition(work_ctx, prompt, clip_skip, width, height);
ggml_tensor* c = cond_pair.first;
ggml_tensor* c_vector = cond_pair.second; // [adm_in_channels, ]
int64_t *ne = c->ne;
fprintf(stderr, "%s: text embedding tensor ne [%ld, %ld, %ld, %ld] \n",
__func__, ne[0], ne[1], ne[2], ne[3]);
ne = c_vector->ne;
fprintf(stderr, "%s: SDXL pooled text embedding tensor ne [%ld, %ld, %ld, %ld] \n",
__func__, ne[0], ne[1], ne[2], ne[3]);
struct ggml_tensor* uc = NULL;
struct ggml_tensor* uc_vector = NULL;
if (cfg_scale != 1.0) {