DarthAffe/stable-diffusion.cpp
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use attention_mask in Flux.2 klein LLMEmbedder

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leejet 2026-01-16 23:33:51 +08:00
parent 6a478d2822
commit 248fc2b193
4 changed files with 78 additions and 21 deletions
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39
conditioner.hpp
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@ -1708,6 +1708,9 @@ struct LLMEmbedder : public Conditioner {
int prompt_template_encode_start_idx = 34;
int max_length = 0;
std::set<int> out_layers;
std::vector<int> tokens;
std::vector<float> weights;
std::vector<float> mask;
if (llm->enable_vision && conditioner_params.ref_images.size() > 0) {
LOG_INFO("QwenImageEditPlusPipeline");
prompt_template_encode_start_idx = 64;
@ -1795,6 +1798,7 @@ struct LLMEmbedder : public Conditioner {
prompt += "<|im_end|>\n<|im_start|>assistant\n";
} else if (version == VERSION_FLUX2_KLEIN) {
prompt_template_encode_start_idx = 0;
max_length = 512;
out_layers = {9, 18, 27};
prompt = "<|im_start|>user\n";
@ -1804,6 +1808,16 @@ struct LLMEmbedder : public Conditioner {
prompt_attn_range.second = static_cast<int>(prompt.size());
prompt += "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n";
auto tokens_and_weights = tokenize(prompt, prompt_attn_range, 0, false);
tokens = std::get<0>(tokens_and_weights);
weights = std::get<1>(tokens_and_weights);
mask.insert(mask.end(), tokens.size(), 1.f);
if (tokens.size() < max_length) {
mask.insert(mask.end(), max_length - tokens.size(), 0.f);
tokenizer->pad_tokens(tokens, weights, max_length, true);
}
} else if (version == VERSION_OVIS_IMAGE) {
prompt_template_encode_start_idx = 28;
max_length = prompt_template_encode_start_idx + 256;
@ -1827,17 +1841,34 @@ struct LLMEmbedder : public Conditioner {
prompt += "<|im_end|>\n<|im_start|>assistant\n";
}
auto tokens_and_weights = tokenize(prompt, prompt_attn_range, max_length, max_length > 0);
auto& tokens = std::get<0>(tokens_and_weights);
auto& weights = std::get<1>(tokens_and_weights);
if (tokens.empty()) {
auto tokens_and_weights = tokenize(prompt, prompt_attn_range, max_length, max_length > 0);
tokens = std::get<0>(tokens_and_weights);
weights = std::get<1>(tokens_and_weights);
}
int64_t t0 = ggml_time_ms();
struct ggml_tensor* hidden_states = nullptr; // [N, n_token, 3584]
auto input_ids = vector_to_ggml_tensor_i32(work_ctx, tokens);
ggml_tensor* attention_mask = nullptr;
if (!mask.empty()) {
attention_mask = ggml_new_tensor_2d(work_ctx, GGML_TYPE_F32, mask.size(), mask.size());
ggml_ext_tensor_iter(attention_mask, [&](ggml_tensor* attention_mask, int64_t i0, int64_t i1, int64_t i2, int64_t i3) {
float value = 0.f;
if (mask[i0] == 0.f) {
value = -INFINITY;
} else if (i0 > i1) {
value = -INFINITY;
}
ggml_ext_tensor_set_f32(attention_mask, value, i0, i1, i2, i3);
});
}
llm->compute(n_threads,
input_ids,
attention_mask,
image_embeds,
out_layers,
&hidden_states,
@ -1861,7 +1892,7 @@ struct LLMEmbedder : public Conditioner {
GGML_ASSERT(hidden_states->ne[1] > prompt_template_encode_start_idx);
int64_t min_length = 0;
if (sd_version_is_flux2(version)) {
if (version == VERSION_FLUX2) {
min_length = 512;
}

4
flux.hpp
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@ -1354,8 +1354,8 @@ namespace Flux {
flux_params.num_heads = static_cast<int>(flux_params.hidden_size / head_dim);
LOG_INFO("double blocks: %d, single blocks: %d, guidance_embed: %s, context_in_dim: %" PRId64
", hidden_size: %" PRId64 ", num_heads: %d",
LOG_INFO("flux: depth = %d, depth_single_blocks = %d, guidance_embed = %s, context_in_dim = %" PRId64
", hidden_size = %" PRId64 ", num_heads = %d",
flux_params.depth,
flux_params.depth_single_blocks,
flux_params.guidance_embed ? "true" : "false",

5
ggml_extend.hpp
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@ -1228,7 +1228,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_attention_ext(struct ggml_context
bool diag_mask_inf = false,
bool skip_reshape = false,
bool flash_attn = false,
float kv_scale = 1.0f) { // avoid overflow
float kv_scale = 1.0f / 128.f) { // avoid overflow
int64_t L_q;
int64_t L_k;
int64_t C;
@ -1348,6 +1348,7 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_ext_attention_ext(struct ggml_context
v = ggml_reshape_3d(ctx, v, L_k, d_head, n_kv_head * N); // [N * n_kv_head, d_head, L_k]
auto kq = ggml_mul_mat(ctx, k, q); // [N * n_head, L_q, L_k]
ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
kq = ggml_scale_inplace(ctx, kq, scale);
if (mask) {
kq = ggml_add_inplace(ctx, kq, mask);
@ -2183,7 +2184,7 @@ public:
bool bias = true,
bool force_f32 = false,
bool force_prec_f32 = false,
float scale = 1.f)
float scale = 1.f / 128.f)
: in_features(in_features),
out_features(out_features),
bias(bias),

51
llm.hpp
View File

@ -837,7 +837,8 @@ namespace LLM {
struct ggml_tensor* forward(GGMLRunnerContext* ctx,
struct ggml_tensor* x,
struct ggml_tensor* input_pos) {
struct ggml_tensor* input_pos,
struct ggml_tensor* attention_mask = nullptr) {
// x: [N, n_token, hidden_size]
int64_t n_token = x->ne[1];
int64_t N = x->ne[2];
@ -880,7 +881,7 @@ namespace LLM {
k = ggml_cont(ctx->ggml_ctx, ggml_ext_torch_permute(ctx->ggml_ctx, k, 0, 2, 1, 3)); // [N, num_kv_heads, n_token, head_dim]
k = ggml_reshape_3d(ctx->ggml_ctx, k, k->ne[0], k->ne[1], k->ne[2] * k->ne[3]); // [N*num_kv_heads, n_token, head_dim]
x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, nullptr, true, true, false); // [N, n_token, hidden_size]
x = ggml_ext_attention_ext(ctx->ggml_ctx, ctx->backend, q, k, v, num_heads, attention_mask, false, true, false); // [N, n_token, hidden_size]
x = out_proj->forward(ctx, x); // [N, n_token, hidden_size]
return x;
@ -898,7 +899,8 @@ namespace LLM {
struct ggml_tensor* forward(GGMLRunnerContext* ctx,
struct ggml_tensor* x,
struct ggml_tensor* input_pos) {
struct ggml_tensor* input_pos,
struct ggml_tensor* attention_mask = nullptr) {
// x: [N, n_token, hidden_size]
auto self_attn = std::dynamic_pointer_cast<Attention>(blocks["self_attn"]);
auto mlp = std::dynamic_pointer_cast<MLP>(blocks["mlp"]);
@ -907,7 +909,7 @@ namespace LLM {
auto residual = x;
x = input_layernorm->forward(ctx, x);
x = self_attn->forward(ctx, x, input_pos);
x = self_attn->forward(ctx, x, input_pos, attention_mask);
x = ggml_add_inplace(ctx->ggml_ctx, x, residual);
residual = x;
@ -936,6 +938,7 @@ namespace LLM {
struct ggml_tensor* forward(GGMLRunnerContext* ctx,
struct ggml_tensor* input_ids,
struct ggml_tensor* input_pos,
struct ggml_tensor* attention_mask,
std::vector<std::pair<int, ggml_tensor*>> image_embeds,
std::set<int> out_layers) {
// input_ids: [N, n_token]
@ -990,7 +993,7 @@ namespace LLM {
for (int i = 0; i < num_layers; i++) {
auto block = std::dynamic_pointer_cast<TransformerBlock>(blocks["layers." + std::to_string(i)]);
x = block->forward(ctx, x, input_pos);
x = block->forward(ctx, x, input_pos, attention_mask);
if (out_layers.find(i + 1) != out_layers.end()) {
intermediate_outputs.push_back(x);
}
@ -1036,12 +1039,13 @@ namespace LLM {
struct ggml_tensor* forward(GGMLRunnerContext* ctx,
struct ggml_tensor* input_ids,
struct ggml_tensor* input_pos,
struct ggml_tensor* attention_mask,
std::vector<std::pair<int, ggml_tensor*>> image_embeds,
std::set<int> out_layers) {
// input_ids: [N, n_token]
auto model = std::dynamic_pointer_cast<TextModel>(blocks["model"]);
auto x = model->forward(ctx, input_ids, input_pos, image_embeds, out_layers);
auto x = model->forward(ctx, input_ids, input_pos, attention_mask, image_embeds, out_layers);
return x;
}
@ -1063,6 +1067,7 @@ namespace LLM {
LLM model;
std::vector<int> input_pos_vec;
std::vector<float> attention_mask_vec;
std::vector<float> window_mask_vec;
std::vector<int> window_index_vec;
std::vector<int> window_inverse_index_vec;
@ -1157,9 +1162,10 @@ namespace LLM {
struct ggml_tensor* forward(GGMLRunnerContext* ctx,
struct ggml_tensor* input_ids,
struct ggml_tensor* input_pos,
struct ggml_tensor* attention_mask,
std::vector<std::pair<int, ggml_tensor*>> image_embeds,
std::set<int> out_layers) {
auto hidden_states = model.forward(ctx, input_ids, input_pos, image_embeds, out_layers); // [N, n_token, hidden_size]
auto hidden_states = model.forward(ctx, input_ids, input_pos, attention_mask, image_embeds, out_layers); // [N, n_token, hidden_size]
return hidden_states;
}
@ -1174,6 +1180,7 @@ namespace LLM {
}
struct ggml_cgraph* build_graph(struct ggml_tensor* input_ids,
struct ggml_tensor* attention_mask,
std::vector<std::pair<int, ggml_tensor*>> image_embeds,
std::set<int> out_layers) {
struct ggml_cgraph* gf = ggml_new_graph(compute_ctx);
@ -1205,9 +1212,26 @@ namespace LLM {
input_pos_vec.size());
set_backend_tensor_data(input_pos, input_pos_vec.data());
if (attention_mask != nullptr) {
attention_mask = to_backend(attention_mask);
} else {
attention_mask_vec.resize(n_tokens * n_tokens);
for (int i0 = 0; i0 < n_tokens; i0++) {
for (int i1 = 0; i1 < n_tokens; i1++) {
float value = 0.f;
if (i0 > i1) {
value = -INFINITY;
}
attention_mask_vec[i1 * n_tokens + i0] = value;
}
}
attention_mask = ggml_new_tensor_2d(compute_ctx, GGML_TYPE_F32, n_tokens, n_tokens);
set_backend_tensor_data(attention_mask, attention_mask_vec.data());
}
auto runner_ctx = get_context();
struct ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, input_pos, image_embeds, out_layers);
struct ggml_tensor* hidden_states = forward(&runner_ctx, input_ids, input_pos, attention_mask, image_embeds, out_layers);
ggml_build_forward_expand(gf, hidden_states);
@ -1216,12 +1240,13 @@ namespace LLM {
bool compute(const int n_threads,
struct ggml_tensor* input_ids,
struct ggml_tensor* attention_mask,
std::vector<std::pair<int, ggml_tensor*>> image_embeds,
std::set<int> out_layers,
ggml_tensor** output,
ggml_context* output_ctx = nullptr) {
auto get_graph = [&]() -> struct ggml_cgraph* {
return build_graph(input_ids, image_embeds, out_layers);
return build_graph(input_ids, attention_mask, image_embeds, out_layers);
};
return GGMLRunner::compute(get_graph, n_threads, true, output, output_ctx);
}
@ -1525,7 +1550,7 @@ namespace LLM {
struct ggml_tensor* out = nullptr;
int64_t t0 = ggml_time_ms();
model.compute(8, input_ids, image_embeds, {}, &out, work_ctx);
model.compute(8, input_ids, nullptr, image_embeds, {}, &out, work_ctx);
int64_t t1 = ggml_time_ms();
print_ggml_tensor(out);
@ -1565,7 +1590,7 @@ namespace LLM {
struct ggml_tensor* out = nullptr;
int64_t t0 = ggml_time_ms();
model.compute(8, input_ids, {}, {10, 20, 30}, &out, work_ctx);
model.compute(8, input_ids, nullptr, {}, {10, 20, 30}, &out, work_ctx);
int64_t t1 = ggml_time_ms();
print_ggml_tensor(out);
@ -1588,7 +1613,7 @@ namespace LLM {
struct ggml_tensor* out = nullptr;
int64_t t0 = ggml_time_ms();
model.compute(8, input_ids, {}, {35}, &out, work_ctx);
model.compute(8, input_ids, nullptr, {}, {35}, &out, work_ctx);
int64_t t1 = ggml_time_ms();
print_ggml_tensor(out);
@ -1611,7 +1636,7 @@ namespace LLM {
struct ggml_tensor* out = nullptr;
int64_t t0 = ggml_time_ms();
model.compute(8, input_ids, {}, {}, &out, work_ctx);
model.compute(8, input_ids, nullptr, {}, {}, &out, work_ctx);
int64_t t1 = ggml_time_ms();
print_ggml_tensor(out);