alphard/stable-diffusion.cpp
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sync: update ggml (#134)

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leejet 2024-01-05 23:18:41 +08:00 committed by GitHub
parent db382348cc
commit 2b6ec97fe2
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9 changed files with 21 additions and 65 deletions
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9
clip.hpp
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@ -443,8 +443,6 @@ struct ResidualAttentionBlock {
struct ggml_tensor* ln2_w; // [hidden_size, ] struct ggml_tensor* ln2_w; // [hidden_size, ]
struct ggml_tensor* ln2_b; // [hidden_size, ] struct ggml_tensor* ln2_b; // [hidden_size, ]
struct ggml_tensor* attn_scale; // [hidden_size, ]
size_t calculate_mem_size(ggml_type wtype) { size_t calculate_mem_size(ggml_type wtype) {
double mem_size = 0; double mem_size = 0;
mem_size += 4 * hidden_size * hidden_size * ggml_type_sizef(wtype); // q_w/k_w/v_w/out_w mem_size += 4 * hidden_size * hidden_size * ggml_type_sizef(wtype); // q_w/k_w/v_w/out_w
@ -452,7 +450,6 @@ struct ResidualAttentionBlock {
mem_size += 2 * hidden_size * intermediate_size * ggml_type_sizef(wtype); // fc1_w/fc2_w mem_size += 2 * hidden_size * intermediate_size * ggml_type_sizef(wtype); // fc1_w/fc2_w
mem_size += intermediate_size * ggml_type_sizef(GGML_TYPE_F32); // fc1_b mem_size += intermediate_size * ggml_type_sizef(GGML_TYPE_F32); // fc1_b
mem_size += hidden_size * ggml_type_sizef(GGML_TYPE_F32); // fc2_b mem_size += hidden_size * ggml_type_sizef(GGML_TYPE_F32); // fc2_b
mem_size += ggml_type_sizef(GGML_TYPE_F32); // attn_scale
return static_cast<size_t>(mem_size); return static_cast<size_t>(mem_size);
} }
@ -479,10 +476,6 @@ struct ResidualAttentionBlock {
ln2_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size); ln2_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
ln2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size); ln2_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hidden_size);
attn_scale = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
ggml_allocr_alloc(alloc, attn_scale);
float scale = 1.0f / sqrt((float)d_model);
ggml_backend_tensor_set(attn_scale, &scale, 0, sizeof(scale));
} }
void map_by_name(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) { void map_by_name(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
@ -521,7 +514,7 @@ struct ResidualAttentionBlock {
// self-attention // self-attention
{ {
struct ggml_tensor* q = ggml_nn_linear(ctx, x, q_w, q_b); struct ggml_tensor* q = ggml_nn_linear(ctx, x, q_w, q_b);
q = ggml_scale_inplace(ctx, q, attn_scale); q = ggml_scale_inplace(ctx, q, 1.0f / sqrt((float)d_model));
q = ggml_reshape_4d(ctx, q, d_model, n_head, n_token, N); // [N, n_token, n_head, d_model] q = ggml_reshape_4d(ctx, q, d_model, n_head, n_token, N); // [N, n_token, n_head, d_model]
q = ggml_cont(ctx, ggml_permute(ctx, q, 0, 2, 1, 3)); // [N, n_head, n_token, d_model] q = ggml_cont(ctx, ggml_permute(ctx, q, 0, 2, 1, 3)); // [N, n_head, n_token, d_model]
q = ggml_reshape_3d(ctx, q, d_model, n_token, n_head * N); // [N * n_head, n_token, d_model] q = ggml_reshape_3d(ctx, q, d_model, n_token, n_head * N); // [N * n_head, n_token, d_model]

15
esrgan.hpp
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@ -91,7 +91,7 @@ struct ResidualDenseBlock {
tensors[prefix + "conv5.bias"] = conv5_b; tensors[prefix + "conv5.bias"] = conv5_b;
} }
ggml_tensor* forward(ggml_context* ctx, ggml_tensor* out_scale, ggml_tensor* x /* feat */) { ggml_tensor* forward(ggml_context* ctx, float out_scale, ggml_tensor* x /* feat */) {
// x1 = self.lrelu(self.conv1(x)) // x1 = self.lrelu(self.conv1(x))
ggml_tensor* x1 = ggml_nn_conv_2d(ctx, x, conv1_w, conv1_b, 1, 1, 1, 1); ggml_tensor* x1 = ggml_nn_conv_2d(ctx, x, conv1_w, conv1_b, 1, 1, 1, 1);
x1 = ggml_leaky_relu(ctx, x1, 0.2f, true); x1 = ggml_leaky_relu(ctx, x1, 0.2f, true);
@ -161,7 +161,7 @@ struct EsrganBlock {
} }
} }
ggml_tensor* forward(ggml_context* ctx, ggml_tensor* out_scale, ggml_tensor* x) { ggml_tensor* forward(ggml_context* ctx, float out_scale, ggml_tensor* x) {
ggml_tensor* out = x; ggml_tensor* out = x;
for (int i = 0; i < num_residual_blocks; i++) { for (int i = 0; i < num_residual_blocks; i++) {
// out = self.rdb...(x) // out = self.rdb...(x)
@ -325,7 +325,7 @@ struct ESRGAN : public GGMLModule {
tensors["conv_last.bias"] = conv_last_b; tensors["conv_last.bias"] = conv_last_b;
} }
ggml_tensor* forward(ggml_context* ctx0, ggml_tensor* out_scale, ggml_tensor* x /* feat */) { ggml_tensor* forward(ggml_context* ctx0, float out_scale, ggml_tensor* x /* feat */) {
// feat = self.conv_first(feat) // feat = self.conv_first(feat)
auto h = ggml_nn_conv_2d(ctx0, x, conv_first_w, conv_first_b, 1, 1, 1, 1); auto h = ggml_nn_conv_2d(ctx0, x, conv_first_w, conv_first_b, 1, 1, 1, 1);
@ -376,12 +376,7 @@ struct ESRGAN : public GGMLModule {
struct ggml_cgraph* gf = ggml_new_graph(ctx0); struct ggml_cgraph* gf = ggml_new_graph(ctx0);
struct ggml_tensor* x_ = NULL; struct ggml_tensor* x_ = NULL;
struct ggml_tensor* os = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1); float out_scale = 0.2f;
ggml_allocr_alloc(compute_allocr, os);
if (!ggml_allocr_is_measure(compute_allocr)) {
float scale = 0.2f;
ggml_backend_tensor_set(os, &scale, 0, sizeof(scale));
}
// it's performing a compute, check if backend isn't cpu // it's performing a compute, check if backend isn't cpu
if (!ggml_backend_is_cpu(backend)) { if (!ggml_backend_is_cpu(backend)) {
@ -397,7 +392,7 @@ struct ESRGAN : public GGMLModule {
x_ = x; x_ = x;
} }
struct ggml_tensor* out = forward(ctx0, os, x); struct ggml_tensor* out = forward(ctx0, out_scale, x);
ggml_build_forward_expand(gf, out); ggml_build_forward_expand(gf, out);
ggml_free(ctx0); ggml_free(ctx0);

2
ggml

@ -1 +1 @@
Subproject commit 9ab842f210f02cdb8ac7a13d02da10cdda683cfc Subproject commit 5e449697f0e9e4c3dff7e66e31bcce37a7517a1b

2
ggml_extend.hpp
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@ -449,7 +449,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_group_norm(struct ggml_context* ct
struct ggml_tensor* w, struct ggml_tensor* w,
struct ggml_tensor* b, struct ggml_tensor* b,
int num_groups = 32) { int num_groups = 32) {
if (x->n_dims == 4) { if (ggml_n_dims(x) >= 3) {
w = ggml_reshape_4d(ctx, w, 1, 1, w->ne[0], 1); w = ggml_reshape_4d(ctx, w, 1, 1, w->ne[0], 1);
b = ggml_reshape_4d(ctx, b, 1, 1, b->ne[0], 1); b = ggml_reshape_4d(ctx, b, 1, 1, b->ne[0], 1);
} }

15
lora.hpp
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@ -113,7 +113,7 @@ struct LoraModel : public GGMLModule {
applied_lora_tensors.insert(scale_name); applied_lora_tensors.insert(scale_name);
// calc_cale // calc_cale
int64_t dim = lora_down->ne[lora_down->n_dims - 1]; int64_t dim = lora_down->ne[ggml_n_dims(lora_down) - 1];
float scale_value = 1.0f; float scale_value = 1.0f;
if (lora_tensors.find(scale_name) != lora_tensors.end()) { if (lora_tensors.find(scale_name) != lora_tensors.end()) {
scale_value = ggml_backend_tensor_get_f32(lora_tensors[scale_name]); scale_value = ggml_backend_tensor_get_f32(lora_tensors[scale_name]);
@ -123,17 +123,10 @@ struct LoraModel : public GGMLModule {
} }
scale_value *= multiplier; scale_value *= multiplier;
ggml_tensor* lora_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
ggml_allocr_alloc(compute_allocr, lora_scale);
if (!ggml_allocr_is_measure(compute_allocr)) {
ggml_backend_tensor_set(lora_scale, &scale_value, 0, ggml_nbytes(lora_scale));
}
// flat lora tensors to multiply it // flat lora tensors to multiply it
int64_t lora_up_rows = lora_up->ne[lora_up->n_dims - 1]; int64_t lora_up_rows = lora_up->ne[ggml_n_dims(lora_up) - 1];
lora_up = ggml_reshape_2d(ctx0, lora_up, ggml_nelements(lora_up) / lora_up_rows, lora_up_rows); lora_up = ggml_reshape_2d(ctx0, lora_up, ggml_nelements(lora_up) / lora_up_rows, lora_up_rows);
int64_t lora_down_rows = lora_down->ne[lora_down->n_dims - 1]; int64_t lora_down_rows = lora_down->ne[ggml_n_dims(lora_down) - 1];
lora_down = ggml_reshape_2d(ctx0, lora_down, ggml_nelements(lora_down) / lora_down_rows, lora_down_rows); lora_down = ggml_reshape_2d(ctx0, lora_down, ggml_nelements(lora_down) / lora_down_rows, lora_down_rows);
// ggml_mul_mat requires tensor b transposed // ggml_mul_mat requires tensor b transposed
@ -142,7 +135,7 @@ struct LoraModel : public GGMLModule {
updown = ggml_cont(ctx0, ggml_transpose(ctx0, updown)); updown = ggml_cont(ctx0, ggml_transpose(ctx0, updown));
updown = ggml_reshape(ctx0, updown, weight); updown = ggml_reshape(ctx0, updown, weight);
GGML_ASSERT(ggml_nelements(updown) == ggml_nelements(weight)); GGML_ASSERT(ggml_nelements(updown) == ggml_nelements(weight));
updown = ggml_scale_inplace(ctx0, updown, lora_scale); updown = ggml_scale_inplace(ctx0, updown, scale_value);
ggml_tensor* final_weight; ggml_tensor* final_weight;
// if (weight->type != GGML_TYPE_F32 && weight->type != GGML_TYPE_F16) { // if (weight->type != GGML_TYPE_F32 && weight->type != GGML_TYPE_F16) {
// final_weight = ggml_new_tensor(ctx0, GGML_TYPE_F32, weight->n_dims, weight->ne); // final_weight = ggml_new_tensor(ctx0, GGML_TYPE_F32, weight->n_dims, weight->ne);

5
model.cpp
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@ -673,7 +673,7 @@ bool ModelLoader::init_from_gguf_file(const std::string& file_path, const std::s
// LOG_DEBUG("%s", name.c_str()); // LOG_DEBUG("%s", name.c_str());
TensorStorage tensor_storage(prefix + name, dummy->type, dummy->ne, dummy->n_dims, file_index, offset); TensorStorage tensor_storage(prefix + name, dummy->type, dummy->ne, ggml_n_dims(dummy), file_index, offset);
GGML_ASSERT(ggml_nbytes(dummy) == tensor_storage.nbytes()); GGML_ASSERT(ggml_nbytes(dummy) == tensor_storage.nbytes());
@ -1417,6 +1417,9 @@ bool ModelLoader::load_tensors(std::map<std::string, struct ggml_tensor*>& tenso
if (pair.first.find("cond_stage_model.transformer.text_model.encoder.layers.23") != std::string::npos) { if (pair.first.find("cond_stage_model.transformer.text_model.encoder.layers.23") != std::string::npos) {
continue; continue;
} }
if (pair.first.find("alphas_cumprod") != std::string::npos) {
continue;
}
if (pair.first.find("alphas_cumprod") != std::string::npos) { if (pair.first.find("alphas_cumprod") != std::string::npos) {
continue; continue;

17
tae.hpp
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@ -278,9 +278,6 @@ struct TinyDecoder {
ggml_tensor* conv_final_w; // [output_channels, channels, 3, 3] ggml_tensor* conv_final_w; // [output_channels, channels, 3, 3]
ggml_tensor* conv_final_b; // [output_channels] ggml_tensor* conv_final_b; // [output_channels]
ggml_tensor* in_scale_1d3; // [1]
ggml_tensor* in_scale_3; // [1]
TinyDecoder() { TinyDecoder() {
for (int i = 0; i < num_blocks; i++) { for (int i = 0; i < num_blocks; i++) {
input_blocks[i].in_channels = channels; input_blocks[i].in_channels = channels;
@ -351,16 +348,6 @@ struct TinyDecoder {
} }
final_block.init_params(ctx); final_block.init_params(ctx);
// initialize constants scales
in_scale_1d3 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
in_scale_3 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
ggml_allocr_alloc(alloc, in_scale_1d3);
float scale_1d3 = 1.0f / 3.0f;
ggml_backend_tensor_set(in_scale_1d3, &scale_1d3, 0, sizeof(scale_1d3));
ggml_allocr_alloc(alloc, in_scale_3);
float scale_3 = 3.0f;
ggml_backend_tensor_set(in_scale_3, &scale_3, 0, sizeof(scale_3));
} }
void map_by_name(std::map<std::string, ggml_tensor*>& tensors, std::string prefix) { void map_by_name(std::map<std::string, ggml_tensor*>& tensors, std::string prefix) {
@ -391,9 +378,9 @@ struct TinyDecoder {
ggml_tensor* forward(ggml_context* ctx, ggml_tensor* z) { ggml_tensor* forward(ggml_context* ctx, ggml_tensor* z) {
// torch.tanh(x / 3) * 3 // torch.tanh(x / 3) * 3
auto h = ggml_scale(ctx, z, in_scale_1d3); auto h = ggml_scale(ctx, z, 1.0f / 3.0f);
h = ggml_tanh_inplace(ctx, h); h = ggml_tanh_inplace(ctx, h);
h = ggml_scale(ctx, h, in_scale_3); h = ggml_scale(ctx, h, 3.0f);
// conv(4, 64) // conv(4, 64)
h = ggml_nn_conv_2d(ctx, h, conv_input_w, conv_input_b, 1, 1, 1, 1); h = ggml_nn_conv_2d(ctx, h, conv_input_w, conv_input_b, 1, 1, 1, 1);

12
unet.hpp
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@ -182,8 +182,6 @@ struct SpatialTransformer {
std::vector<Transformer> transformers; std::vector<Transformer> transformers;
struct ggml_tensor* attn_scale;
// proj_out // proj_out
struct ggml_tensor* proj_out_w; // [in_channels, in_channels, 1, 1] struct ggml_tensor* proj_out_w; // [in_channels, in_channels, 1, 1]
struct ggml_tensor* proj_out_b; // [in_channels,] struct ggml_tensor* proj_out_b; // [in_channels,]
@ -202,7 +200,6 @@ struct SpatialTransformer {
mem_size += 2 * in_channels * ggml_type_sizef(GGML_TYPE_F32); // norm_w/norm_b mem_size += 2 * in_channels * ggml_type_sizef(GGML_TYPE_F32); // norm_w/norm_b
mem_size += 2 * in_channels * in_channels * 1 * 1 * ggml_type_sizef(GGML_TYPE_F16); // proj_in_w/proj_out_w mem_size += 2 * in_channels * in_channels * 1 * 1 * ggml_type_sizef(GGML_TYPE_F16); // proj_in_w/proj_out_w
mem_size += 2 * in_channels * ggml_type_sizef(GGML_TYPE_F32); // proj_in_b/proj_out_b mem_size += 2 * in_channels * ggml_type_sizef(GGML_TYPE_F32); // proj_in_b/proj_out_b
mem_size += 1 * ggml_type_sizef(GGML_TYPE_F32); // attn_scale
// transformer // transformer
for (auto& transformer : transformers) { for (auto& transformer : transformers) {
@ -226,11 +223,6 @@ struct SpatialTransformer {
proj_out_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 1, 1, in_channels, in_channels); proj_out_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 1, 1, in_channels, in_channels);
proj_out_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels); proj_out_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
attn_scale = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
ggml_allocr_alloc(alloc, attn_scale);
float scale = 1.0f / sqrt((float)d_head);
ggml_backend_tensor_set(attn_scale, &scale, 0, sizeof(scale));
// transformer // transformer
for (auto& transformer : transformers) { for (auto& transformer : transformers) {
transformer.norm1_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels); transformer.norm1_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
@ -332,7 +324,7 @@ struct SpatialTransformer {
x = ggml_reshape_2d(ctx, x, c, h * w * n); // [N * h * w, in_channels] x = ggml_reshape_2d(ctx, x, c, h * w * n); // [N * h * w, in_channels]
struct ggml_tensor* q = ggml_mul_mat(ctx, transformer.attn1_q_w, x); // [N * h * w, in_channels] struct ggml_tensor* q = ggml_mul_mat(ctx, transformer.attn1_q_w, x); // [N * h * w, in_channels]
#if !defined(SD_USE_FLASH_ATTENTION) || defined(SD_USE_CUBLAS) || defined(SD_USE_METAL) #if !defined(SD_USE_FLASH_ATTENTION) || defined(SD_USE_CUBLAS) || defined(SD_USE_METAL)
q = ggml_scale_inplace(ctx, q, attn_scale); q = ggml_scale_inplace(ctx, q, 1.0f / sqrt((float)d_head));
#endif #endif
q = ggml_reshape_4d(ctx, q, d_head, n_head, h * w, n); // [N, h * w, n_head, d_head] q = ggml_reshape_4d(ctx, q, d_head, n_head, h * w, n); // [N, h * w, n_head, d_head]
q = ggml_cont(ctx, ggml_permute(ctx, q, 0, 2, 1, 3)); // [N, n_head, h * w, d_head] q = ggml_cont(ctx, ggml_permute(ctx, q, 0, 2, 1, 3)); // [N, n_head, h * w, d_head]
@ -380,7 +372,7 @@ struct SpatialTransformer {
context = ggml_reshape_2d(ctx, context, context->ne[0], context->ne[1] * context->ne[2]); // [N * max_position, hidden_size] context = ggml_reshape_2d(ctx, context, context->ne[0], context->ne[1] * context->ne[2]); // [N * max_position, hidden_size]
struct ggml_tensor* q = ggml_mul_mat(ctx, transformer.attn2_q_w, x); // [N * h * w, in_channels] struct ggml_tensor* q = ggml_mul_mat(ctx, transformer.attn2_q_w, x); // [N * h * w, in_channels]
#if !defined(SD_USE_FLASH_ATTENTION) || defined(SD_USE_CUBLAS) || defined(SD_USE_METAL) #if !defined(SD_USE_FLASH_ATTENTION) || defined(SD_USE_CUBLAS) || defined(SD_USE_METAL)
q = ggml_scale_inplace(ctx, q, attn_scale); q = ggml_scale_inplace(ctx, q, 1.0f / sqrt((float)d_head));
#endif #endif
q = ggml_reshape_4d(ctx, q, d_head, n_head, h * w, n); // [N, h * w, n_head, d_head] q = ggml_reshape_4d(ctx, q, d_head, n_head, h * w, n); // [N, h * w, n_head, d_head]
q = ggml_cont(ctx, ggml_permute(ctx, q, 0, 2, 1, 3)); // [N, n_head, h * w, d_head] q = ggml_cont(ctx, ggml_permute(ctx, q, 0, 2, 1, 3)); // [N, n_head, h * w, d_head]

9
vae.hpp
View File

@ -118,8 +118,6 @@ struct AttnBlock {
struct ggml_tensor* proj_out_w; // [in_channels, in_channels, 1, 1] struct ggml_tensor* proj_out_w; // [in_channels, in_channels, 1, 1]
struct ggml_tensor* proj_out_b; // [in_channels,] struct ggml_tensor* proj_out_b; // [in_channels,]
struct ggml_tensor* attn_scale;
size_t calculate_mem_size(ggml_type wtype) { size_t calculate_mem_size(ggml_type wtype) {
double mem_size = 0; double mem_size = 0;
mem_size += 6 * in_channels * ggml_type_sizef(GGML_TYPE_F32); // norm_w/norm_b/q_b/k_v/v_b/proj_out_b mem_size += 6 * in_channels * ggml_type_sizef(GGML_TYPE_F32); // norm_w/norm_b/q_b/k_v/v_b/proj_out_b
@ -140,11 +138,6 @@ struct AttnBlock {
proj_out_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 1, 1, in_channels, in_channels); proj_out_w = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, 1, 1, in_channels, in_channels);
proj_out_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels); proj_out_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, in_channels);
attn_scale = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
ggml_allocr_alloc(alloc, attn_scale);
float scale = 1.0f / sqrt((float)in_channels);
ggml_backend_tensor_set(attn_scale, &scale, 0, sizeof(scale));
} }
void map_by_name(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) { void map_by_name(std::map<std::string, struct ggml_tensor*>& tensors, const std::string prefix) {
@ -181,7 +174,7 @@ struct AttnBlock {
k = ggml_reshape_3d(ctx, k, c, h * w, n); // [N, h * w, in_channels] k = ggml_reshape_3d(ctx, k, c, h * w, n); // [N, h * w, in_channels]
auto w_ = ggml_mul_mat(ctx, k, q); // [N, h * w, h * w] auto w_ = ggml_mul_mat(ctx, k, q); // [N, h * w, h * w]
w_ = ggml_scale_inplace(ctx, w_, attn_scale); w_ = ggml_scale_inplace(ctx, w_, 1.0f / sqrt((float)in_channels));
w_ = ggml_soft_max_inplace(ctx, w_); w_ = ggml_soft_max_inplace(ctx, w_);
v = ggml_reshape_3d(ctx, v, h * w, c, n); // [N, in_channels, h * w] v = ggml_reshape_3d(ctx, v, h * w, c, n); // [N, in_channels, h * w]