DarthAffe/stable-diffusion.cpp
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optimize the handling of the FeedForward precision fix

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leejet 2025-10-12 16:36:55 +08:00
parent 98d6e71492
commit cc064a0530
2 changed files with 24 additions and 14 deletions
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20
common.hpp
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@ -243,9 +243,8 @@ public:
int64_t dim_out,
int64_t mult = 4,
Activation activation = Activation::GEGLU,
bool force_prec_f32 = false) {
bool precision_fix = false) {
int64_t inner_dim = dim * mult;
SD_UNUSED(force_prec_f32);
if (activation == Activation::GELU) {
blocks["net.0"] = std::shared_ptr<GGMLBlock>(new GELU(dim, inner_dim));
} else {
@ -253,7 +252,14 @@ public:
}
// net_1 is nn.Dropout(), skip for inference
blocks["net.2"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, dim_out));
float scale = 1.f;
if (precision_fix) {
scale = 1.f / 128.f;
}
// The purpose of the scale here is to prevent NaN issues in certain situations.
// For example, when using Vulkan without enabling force_prec_f32,
// or when using CUDA but the weights are k-quants.
blocks["net.2"] = std::shared_ptr<GGMLBlock>(new Linear(inner_dim, dim_out, true, false, false, scale));
}
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
@ -264,13 +270,7 @@ public:
auto net_2 = std::dynamic_pointer_cast<Linear>(blocks["net.2"]);
x = net_0->forward(ctx, x); // [ne3, ne2, ne1, inner_dim]
// The purpose of the scale here is to prevent NaN issues in certain situations.
// For example, when using Vulkan without enabling force_prec_f32,
// or when using CUDA but the weights are k-quants.
float scale = 1.f / 128.f;
x = ggml_scale(ctx, x, scale);
x = net_2->forward(ctx, x); // [ne3, ne2, ne1, dim_out]
x = ggml_scale(ctx, x, 1.f / scale);
x = net_2->forward(ctx, x); // [ne3, ne2, ne1, dim_out]
return x;
}
};

18
ggml_extend.hpp
View File

@ -944,11 +944,18 @@ __STATIC_INLINE__ struct ggml_tensor* ggml_nn_linear(struct ggml_context* ctx,
struct ggml_tensor* x,
struct ggml_tensor* w,
struct ggml_tensor* b,
bool force_prec_f32 = false) {
bool force_prec_f32 = false,
float scale = 1.f) {
if (scale != 1.f) {
x = ggml_scale(ctx, x, scale);
}
x = ggml_mul_mat(ctx, w, x);
if (force_prec_f32) {
ggml_mul_mat_set_prec(x, GGML_PREC_F32);
}
if (scale != 1.f) {
x = ggml_scale(ctx, x, 1.f / scale);
}
if (b != NULL) {
x = ggml_add_inplace(ctx, x, b);
}
@ -1962,6 +1969,7 @@ protected:
bool bias;
bool force_f32;
bool force_prec_f32;
float scale;
void init_params(struct ggml_context* ctx, const String2GGMLType& tensor_types = {}, const std::string prefix = "") {
enum ggml_type wtype = get_type(prefix + "weight", tensor_types, GGML_TYPE_F32);
@ -1980,12 +1988,14 @@ public:
int64_t out_features,
bool bias = true,
bool force_f32 = false,
bool force_prec_f32 = false)
bool force_prec_f32 = false,
float scale = 1.f)
: in_features(in_features),
out_features(out_features),
bias(bias),
force_f32(force_f32),
force_prec_f32(force_prec_f32) {}
force_prec_f32(force_prec_f32),
scale(scale) {}
struct ggml_tensor* forward(struct ggml_context* ctx, struct ggml_tensor* x) {
struct ggml_tensor* w = params["weight"];
@ -1993,7 +2003,7 @@ public:
if (bias) {
b = params["bias"];
}
return ggml_nn_linear(ctx, x, w, b, force_prec_f32);
return ggml_nn_linear(ctx, x, w, b, force_prec_f32, scale);
}
};