upstream-archive/stable-diffusion.cpp
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fixed bugs; now failed with cuda error; likely out-of-mem on GPU

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bssrdf
2024-02-25 22:55:26 -05:00
parent d35861b501
commit b556d4411c
5 changed files with 232 additions and 33 deletions
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142
clip.hpp
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@@ -525,27 +525,61 @@ protected:
public:
CLIPLayer(int64_t d_model,
int64_t n_head,
int64_t intermediate_size)
int64_t intermediate_size,
bool atten1 = true)
: d_model(d_model),
n_head(n_head),
intermediate_size(intermediate_size) {
blocks["self_attn"] = std::shared_ptr<GGMLBlock>(new MultiheadAttention(d_model, n_head));
if(atten1)
blocks["self_attn"] = std::shared_ptr<GGMLBlock>(new MultiheadAttention(d_model, n_head));
else
blocks["self_attn"] = std::shared_ptr<GGMLBlock>(new MultiheadAttention2(d_model, n_head));
blocks["layer_norm1"] = std::shared_ptr<GGMLBlock>(new LayerNorm(d_model));
blocks["layer_norm2"] = std::shared_ptr<GGMLBlock>(new LayerNorm(d_model));
blocks["mlp"] = std::shared_ptr<GGMLBlock>(new CLIPMLP(d_model, intermediate_size));
}
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, bool mask = true) {
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, bool mask = true, bool atten1 = true) {
// x: [N, n_token, d_model]
auto self_attn = std::dynamic_pointer_cast<MultiheadAttention>(blocks["self_attn"]);
auto layer_norm1 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm1"]);
auto layer_norm2 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm2"]);
auto mlp = std::dynamic_pointer_cast<CLIPMLP>(blocks["mlp"]);
x = ggml_add(ctx, x, self_attn->forward(ctx, layer_norm1->forward(ctx, x), mask));
x = ggml_add(ctx, x, mlp->forward(ctx, layer_norm2->forward(ctx, x)));
return x;
if(atten1){
auto self_attn = std::dynamic_pointer_cast<MultiheadAttention>(blocks["self_attn"]);
auto layer_norm1 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm1"]);
auto layer_norm2 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm2"]);
auto mlp = std::dynamic_pointer_cast<CLIPMLP>(blocks["mlp"]);
// struct ggml_tensor* r = x;
struct ggml_tensor* h = layer_norm1->forward(ctx, x);
h = self_attn->forward(ctx, h, mask);
// x = ggml_add(ctx, x, self_attn->forward(ctx, layer_norm1->forward(ctx, x), mask));
x = ggml_add(ctx, x, h);
x = ggml_add(ctx, x, mlp->forward(ctx, layer_norm2->forward(ctx, x)));
return x;
}
else{
auto self_attn = std::dynamic_pointer_cast<MultiheadAttention2>(blocks["self_attn"]);
auto layer_norm1 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm1"]);
auto layer_norm2 = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm2"]);
auto mlp = std::dynamic_pointer_cast<CLIPMLP>(blocks["mlp"]);
// struct ggml_tensor* r = x;
struct ggml_tensor* h = layer_norm1->forward(ctx, x);
h = self_attn->forward(ctx, h, mask);
// x = ggml_add(ctx, x, self_attn->forward(ctx, layer_norm1->forward(ctx, x), mask));
x = ggml_add(ctx, x, h);
x = ggml_add(ctx, x, mlp->forward(ctx, layer_norm2->forward(ctx, x)));
return x;
}
}
};
@@ -557,15 +591,16 @@ public:
CLIPEncoder(int64_t n_layer,
int64_t d_model,
int64_t n_head,
int64_t intermediate_size)
int64_t intermediate_size,
bool atten1 = true)
: n_layer(n_layer) {
for (int i = 0; i < n_layer; i++) {
std::string name = "layers." + std::to_string(i);
blocks[name] = std::shared_ptr<GGMLBlock>(new CLIPLayer(d_model, n_head, intermediate_size));
std::string name = "layers." + std::to_string(i);
blocks[name] = std::shared_ptr<GGMLBlock>(new CLIPLayer(d_model, n_head, intermediate_size, atten1));
}
}
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, int clip_skip = -1, bool mask = true) {
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, int clip_skip = -1, bool mask = true, bool atten1 = true) {
// x: [N, n_token, d_model]
int layer_idx = n_layer - 1;
LOG_DEBUG("clip_skip %d", clip_skip);
@@ -579,8 +614,10 @@ public:
break;
}
std::string name = "layers." + std::to_string(i);
// printf(" about to do %s\n", name.c_str());
auto layer = std::dynamic_pointer_cast<CLIPLayer>(blocks[name]);
x = layer->forward(ctx, x); // [N, n_token, d_model]
x = layer->forward(ctx, x, mask, atten1); // [N, n_token, d_model]
// print_ggml_tensor(x, true, ("layer "+std::to_string(i)).c_str());
// LOG_DEBUG("layer %d", i);
}
return x;
@@ -779,7 +816,7 @@ public:
class CLIPVisionModel : public GGMLBlock {
protected:
void init_params(struct ggml_context* ctx, ggml_type wtype) {
params["visual_projection"] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, projection_dim, hidden_size);
params["visual_projection.weight"] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, projection_dim, hidden_size);
}
public:
@@ -837,10 +874,77 @@ public:
}
return pooled; // [N, projection_dim]
}
};
class CLIPVisionModel2 : public GGMLBlock {
protected:
void init_params(struct ggml_context* ctx, ggml_type wtype) {
params["visual_projection.weight"] = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hidden_size, projection_dim);
}
public:
// network hparams
int32_t num_channels = 3;
int32_t patch_size = 14;
int32_t image_size = 224;
int32_t num_positions = 257; // (image_size / patch_size)^2 + 1
int32_t hidden_size = 1024;
int32_t intermediate_size = 4096;
int32_t n_head = 16;
int32_t n_layer = 24;
int32_t projection_dim = 768;
public:
CLIPVisionModel2(CLIPVersion version = OPENAI_CLIP_VIT_L_14) {
if (version == OPEN_CLIP_VIT_H_14) {
hidden_size = 1280;
intermediate_size = 5120;
n_head = 16;
n_layer = 32;
projection_dim = 1024;
} else if (version == OPEN_CLIP_VIT_BIGG_14) {
hidden_size = 1664;
intermediate_size = 8192;
n_head = 16;
n_layer = 48;
}
blocks["embeddings"] = std::shared_ptr<GGMLBlock>(new CLIPVisionEmbeddings(hidden_size, num_channels, patch_size, image_size));
blocks["pre_layernorm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
blocks["encoder"] = std::shared_ptr<GGMLBlock>(new CLIPEncoder(n_layer, hidden_size, n_head, intermediate_size, false));
blocks["post_layernorm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(hidden_size));
}
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* pixel_values) {
// pixel_values: [N, num_channels, image_size, image_size]
// return: // [N, projection_dim]
auto embeddings = std::dynamic_pointer_cast<CLIPVisionEmbeddings>(blocks["embeddings"]);
auto pre_layernorm = std::dynamic_pointer_cast<LayerNorm>(blocks["pre_layernorm"]);
auto encoder = std::dynamic_pointer_cast<CLIPEncoder>(blocks["encoder"]);
auto post_layernorm = std::dynamic_pointer_cast<LayerNorm>(blocks["post_layernorm"]);
auto x = embeddings->forward(ctx, pixel_values); // [N, num_positions, embed_dim]
print_ggml_tensor(x, true, "embedding");
x = pre_layernorm->forward(ctx, x);
print_ggml_tensor(x, true, "pre_layernorm");
x = encoder->forward(ctx, x, -1, false, false);
print_ggml_tensor(x, true, "encoder");
x = post_layernorm->forward(ctx, x); // [N, n_token, hidden_size]
print_ggml_tensor(x, true, "post_layernorm");
GGML_ASSERT(x->ne[3] == 1);
// int64_t max_token_idx = 0;
// ggml_tensor* pooled = ggml_view_1d(ctx, x, x->ne[0], x->nb[1] * max_token_idx); // assert N == 1
x = ggml_cont(ctx, ggml_permute(ctx, x, 0, 2, 1, 3));
// ggml_view_2d(ctx, w, w->ne[0], w->ne[1] / 2, w->nb[1], 0);
ggml_tensor* pooled = ggml_view_2d(ctx, x, x->ne[0], x->ne[1], x->nb[1], 0);
print_ggml_tensor(pooled, true, "pooled");
return pooled; // [N, projection_dim]
}
struct ggml_tensor* visual_project(struct ggml_context* ctx, struct ggml_tensor* input){
auto visual_projection = params["visual_projection"];
auto pooled = ggml_mul_mat(ctx, ggml_cont(ctx, ggml_transpose(ctx, visual_projection)), input);
auto visual_projection = params["visual_projection.weight"];
auto pooled = ggml_mul_mat(ctx, visual_projection, input);
return pooled;
}

67
ggml_extend.hpp
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@@ -1037,13 +1037,16 @@ public:
}
for (auto& pair : blocks) {
auto& block = pair.second;
// if(starts_with(prefix, "pmid"))
// printf("block pair.first: %s, %s \n", prefix.c_str(), pair.first.c_str());
block->get_param_tensors(tensors, prefix + pair.first);
}
for (auto& pair : params) {
struct ggml_tensor* param = pair.second;
// if(starts_with(prefix, "pmid"))
// printf("params pair.first: %s, %s \n", prefix.c_str(), pair.first.c_str());
tensors[prefix + pair.first] = pair.second;
}
}
@@ -1313,4 +1316,64 @@ public:
}
};
class MultiheadAttention2 : public GGMLBlock {
protected:
int64_t embed_dim;
int64_t n_head;
bool bias;
bool mask;
public:
MultiheadAttention2(int64_t embed_dim,
int64_t n_head,
bool bias = true)
: embed_dim(embed_dim),
n_head(n_head),
bias(bias) {
// printf("MultiheadAttention2: %d, %d, %d\n", embed_dim, n_head, bias?1:0);
blocks["q_proj"] = std::shared_ptr<GGMLBlock>(new Linear(embed_dim, embed_dim, bias));
blocks["k_proj"] = std::shared_ptr<GGMLBlock>(new Linear(embed_dim, embed_dim, bias));
blocks["v_proj"] = std::shared_ptr<GGMLBlock>(new Linear(embed_dim, embed_dim, bias));
blocks["out_proj"] = std::shared_ptr<GGMLBlock>(new Linear(embed_dim, embed_dim, bias));
}
// x: [N, n_token, embed_dim]
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x, bool mask = false) {
auto q_proj = std::dynamic_pointer_cast<Linear>(blocks["q_proj"]);
auto k_proj = std::dynamic_pointer_cast<Linear>(blocks["k_proj"]);
auto v_proj = std::dynamic_pointer_cast<Linear>(blocks["v_proj"]);
auto out_proj = std::dynamic_pointer_cast<Linear>(blocks["out_proj"]);
int64_t N = x->ne[2];
int64_t n_token = x->ne[1];
int64_t d_head = embed_dim / n_head;
struct ggml_tensor* q = q_proj->forward(ctx, x);
q = ggml_reshape_4d(ctx, q, d_head, n_head, n_token, N); // [N, n_token, n_head, d_head]
q = ggml_cont(ctx, ggml_permute(ctx, q, 0, 2, 1, 3)); // [N, n_head, n_token, d_head]
q = ggml_reshape_3d(ctx, q, d_head, n_token, n_head * N); // [N * n_head, n_token, d_head]
struct ggml_tensor* k = k_proj->forward(ctx, x);
k = ggml_reshape_4d(ctx, k, d_head, n_head, n_token, N); // [N, n_token, n_head, d_head]
k = ggml_cont(ctx, ggml_permute(ctx, k, 0, 2, 1, 3)); // [N, n_head, n_token, d_head]
// k = ggml_reshape_3d(ctx, k, d_head, n_token, n_head); // [N * n_head, n_token, d_head] !!BUG!!
k = ggml_reshape_3d(ctx, k, d_head, n_token, n_head * N); // [N * n_head, n_token, d_head]
struct ggml_tensor* v = v_proj->forward(ctx, x);
v = ggml_reshape_4d(ctx, v, d_head, n_head, n_token, N); // [N, n_token, n_head, d_head]
v = ggml_cont(ctx, ggml_permute(ctx, v, 1, 2, 0, 3)); // [N, n_head, d_head, n_token]
v = ggml_reshape_3d(ctx, v, n_token, d_head, n_head * N); // [N * n_head, d_head, n_token]
struct ggml_tensor* kqv = ggml_nn_attention(ctx, q, k, v, mask); // [N * n_head, n_token, d_head]
kqv = ggml_reshape_4d(ctx, kqv, d_head, n_token, n_head, N);
kqv = ggml_cont(ctx, ggml_permute(ctx, kqv, 0, 2, 1, 3)); // [N, n_token, n_head, d_head]
x = ggml_reshape_3d(ctx, kqv, d_head * n_head, n_token, N); // [N * n_token, d_head * n_head]
x = out_proj->forward(ctx, x);
return x;
}
};
#endif // __GGML_EXTEND__HPP__

12
model.cpp
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@@ -828,7 +828,17 @@ bool ModelLoader::init_from_safetensors_file(const std::string& file_path, const
return false;
}
}
// fix photomaker model file to remove the following name fix
size_t pos = name.find("layrnorm");
if(pos != std::string::npos){
name.replace(pos, strlen("layrnorm"), "layernorm");
}
if(starts_with(prefix, "pmid")){
pos = name.find("visual_projection.w");
if(pos != std::string::npos){
name.replace(0, 0, "vision_model.");
}
}
TensorStorage tensor_storage(prefix + name, type, ne, n_dims, file_index, ST_HEADER_SIZE_LEN + header_size_ + begin);
tensor_storage.reverse_ne();

38
pmid.hpp
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@@ -34,7 +34,7 @@ public:
// blocks["fc2"] = std::shared_ptr<GGMLBlock>(new Linear(intermediate_size, d_model));
blocks["fc1"] = std::shared_ptr<GGMLBlock>(new Linear(in_dim, hidden_dim, true));
blocks["fc2"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_dim, out_dim, true));
blocks["layer_norm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(in_dim));
blocks["layernorm"] = std::shared_ptr<GGMLBlock>(new LayerNorm(in_dim));
}
@@ -97,7 +97,7 @@ public:
auto fc1 = std::dynamic_pointer_cast<Linear>(blocks["fc1"]);
auto fc2 = std::dynamic_pointer_cast<Linear>(blocks["fc2"]);
auto layer_norm = std::dynamic_pointer_cast<LayerNorm>(blocks["layer_norm"]);
auto layer_norm = std::dynamic_pointer_cast<LayerNorm>(blocks["layernorm"]);
struct ggml_tensor* r = x;
// x = ggml_nn_layer_norm(ctx, x, ln_w, ln_b);
@@ -177,6 +177,12 @@ public:
// return mem_size;
// }
// void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
// vision_model.get_param_tensors(tensors, prefix+".vision_model");
// fuse_module.get_param_tensors(tensors, prefix+".fuse_module");
// visual_projection_2.get_param_tensors(tensors, prefix);
// }
struct ggml_tensor* fuse_fn(struct ggml_context* ctx,
struct ggml_tensor* prompt_embeds,
struct ggml_tensor* id_embeds) {
@@ -192,6 +198,7 @@ public:
auto stacked_id_embeds = ggml_concat(ctx, prompt_embeds0, id_embeds0);
stacked_id_embeds = ggml_cont(ctx, ggml_permute(ctx, stacked_id_embeds, 1, 2, 0, 3));
// stacked_id_embeds = mlp1.forward(ctx, stacked_id_embeds);
// stacked_id_embeds = ggml_add(ctx, stacked_id_embeds, prompt_embeds);
// stacked_id_embeds = mlp2.forward(ctx, stacked_id_embeds);
@@ -219,7 +226,6 @@ public:
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);
struct ggml_tensor *stacked_id_embeds = fuse_fn(ctx, image_token_embeds, valid_id_embeds);
stacked_id_embeds = ggml_cont(ctx, ggml_permute(ctx, stacked_id_embeds, 0, 2, 1, 3));
@@ -254,11 +260,18 @@ public:
blocks["visual_projection_2"] = std::shared_ptr<GGMLBlock>(new Linear(hidden_size, projection_dim, false));
}
// void init_params(struct ggml_context* ctx, ggml_type wtype) {
// params["visual_projection_2.weight"] = ggml_new_tensor_2d(ctx, wtype, hidden_size, projection_dim);
// }
struct ggml_tensor* forward(struct ggml_context* ctx,
struct ggml_tensor* pixel_values){
auto visual_projection_2 = std::dynamic_pointer_cast<Linear>(blocks["visual_projection_2"]);
// auto visual_projection_2 = params["visual_projection_2.weight"];
auto x = visual_projection_2->forward(ctx, pixel_values); // [N, projection_dim]
// auto x = ggml_mul_mat(ctx, visual_projection_2, pixel_values);
return x; // [N, projection_dim]
}
@@ -270,7 +283,7 @@ struct PhotoMakerIDEncoder : public GGMLModule {
public:
SDVersion version = VERSION_XL;
CLIPVisionModel vision_model;
CLIPVisionModel2 vision_model;
FuseModule fuse_module;
// struct ggml_tensor* visual_projection_2;
VisualProjection visual_projection_2;
@@ -286,8 +299,11 @@ public:
style_strength(sty){
// vision_model = CLIPVisionModel();
vision_model.init(params_ctx, wtype);
fuse_module.init(params_ctx, wtype);
visual_projection_2.init(params_ctx, wtype);
}
std::string get_desc() {
return "pmid";
}
@@ -342,8 +358,8 @@ public:
void get_param_tensors(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
vision_model.get_param_tensors(tensors, prefix);
fuse_module.get_param_tensors(tensors, prefix);
vision_model.get_param_tensors(tensors, prefix+".vision_model");
fuse_module.get_param_tensors(tensors, prefix+".fuse_module");
visual_projection_2.get_param_tensors(tensors, prefix);
}
@@ -365,14 +381,15 @@ public:
// class_embedding_temp,
// positions
// ); // [batch_size, seq_length, hidden_size]
struct ggml_tensor *shared_id_embeds = vision_model.forward(ctx,
id_pixel_values, false); // [batch_size, seq_length, hidden_size]
struct ggml_tensor *shared_id_embeds = vision_model.forward(ctx, id_pixel_values); // [batch_size, seq_length, hidden_size]
print_ggml_tensor(shared_id_embeds, true, "shared_id_embeds");
struct ggml_tensor *id_embeds = vision_model.visual_project(ctx, shared_id_embeds); // [batch_size, seq_length, proj_dim(768)]
print_ggml_tensor(id_embeds, true, "id_embeds");
// struct ggml_tensor *id_embeds_2 = ggml_mul_mat(ctx, visual_projection_2, shared_id_embeds); // [batch_size, seq_length, 1280]
struct ggml_tensor *id_embeds_2 = visual_projection_2.forward(ctx, shared_id_embeds); // [batch_size, seq_length, 1280]
print_ggml_tensor(id_embeds_2, true, "id_embeds_2");
@@ -382,11 +399,14 @@ public:
id_embeds = ggml_concat(ctx, id_embeds, id_embeds_2); // [batch_size, seq_length, 1, 2048] check whether concat at dim 2 is right
id_embeds = ggml_cont(ctx, ggml_permute(ctx, id_embeds, 1, 2, 0, 3));
print_ggml_tensor(id_embeds, true, "id_embeds_after_cont+perm");
struct ggml_tensor * updated_prompt_embeds = fuse_module.forward(ctx,
prompt_embeds, id_embeds,
class_tokens_mask,
class_tokens_mask_pos,
left, right);
print_ggml_tensor(updated_prompt_embeds, true, "updated_prompt_embeds");
return updated_prompt_embeds;

6
stable-diffusion.cpp
View File

@@ -278,7 +278,7 @@ public:
}
// LOG_INFO("pmid param memory buffer size = %.2fMB ",
// pmid_model->params_buffer_size / 1024.0 / 1024.0);
pmid_model->get_param_tensors(tensors, "pmid.");
pmid_model->get_param_tensors(tensors, "pmid");
}
// if(stacked_id){
// pmid_model.init_params(GGML_TYPE_F32);
@@ -1643,6 +1643,7 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
// if (sd_ctx->sd->free_params_immediately) {
// sd_ctx->sd->pmid_lora.free_params_buffer();
// }
LOG_INFO("pmid_lora apply completed");
}
struct ggml_init_params params;
@@ -1693,12 +1694,13 @@ sd_image_t* txt2img(sd_ctx_t* sd_ctx,
auto cond_tup = sd_ctx->sd->get_learned_condition_with_trigger(work_ctx, prompt,
clip_skip, width, height, num_input_images );
LOG_INFO("get_learned_condition_with_trigger finished");
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); //
prompts_embeds = sd_ctx->sd->id_encoder(work_ctx, init_img, prompts_embeds, class_tokens_mask);
LOG_INFO("id_encoder");
// if (sd_ctx->sd->free_params_immediately) {
// sd_ctx->sd->pmid_model.free_params_buffer();
// }