upstream-archive/stable-diffusion.cpp
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feat: load scaled FP8 weights without upfront conversion (#1913)

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leejet
2026-08-27 20:50:47 +08:00
committed by GitHub
parent 50d6405683
commit be0e34480d
9 changed files with 73 additions and 101 deletions
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src/core/ggml_extend.hpp
+39 -17
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@@ -3407,7 +3407,6 @@ protected:
bool bias;
bool force_f32;
bool force_prec_f32;
bool allow_weight_scale;
bool has_weight_scale = false;
bool int8_convrot = false;
int int8_convrot_group_size = 0;
@@ -3430,8 +3429,11 @@ protected:
}
auto weight_storage = tensor_storage_map.find(prefix + "weight");
const bool is_int8_tensorwise = weight_storage != tensor_storage_map.end() && weight_storage->second.is_int8_tensorwise;
if ((allow_weight_scale || is_int8_tensorwise) && tensor_storage_map.find(prefix + "weight_scale") != tensor_storage_map.end()) {
params["weight_scale"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, out_features);
auto weight_scale_storage = tensor_storage_map.find(prefix + "weight_scale");
if (weight_scale_storage != tensor_storage_map.end()) {
const int64_t scale_nelements = weight_scale_storage->second.nelements();
GGML_ASSERT(scale_nelements == 1 || scale_nelements == out_features);
params["weight_scale"] = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, scale_nelements);
has_weight_scale = true;
}
if (is_int8_tensorwise) {
@@ -3445,17 +3447,15 @@ protected:
public:
Linear(int64_t in_features,
int64_t out_features,
bool bias = true,
bool force_f32 = false,
bool force_prec_f32 = false,
float scale = 1.f,
bool allow_weight_scale = false)
bool bias = true,
bool force_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),
allow_weight_scale(allow_weight_scale),
scale(scale) {}
void set_scale(float scale_) {
@@ -3467,7 +3467,11 @@ public:
}
ggml_tensor* forward(GGMLRunnerContext* ctx, ggml_tensor* x) override {
ggml_tensor* w = params["weight"];
ggml_tensor* w = params["weight"];
ggml_tensor* weight_scale = has_weight_scale ? params["weight_scale"] : nullptr;
if (w->type == GGML_TYPE_F8_E4M3 || w->type == GGML_TYPE_F8_E5M2) {
w = ggml_cast(ctx->ggml_ctx, w, GGML_TYPE_BF16);
}
ggml_tensor* b = nullptr;
if (bias) {
b = params["bias"];
@@ -3498,7 +3502,7 @@ public:
out = ggml_ext_linear_i8_tensorwise(ctx->ggml_ctx,
x,
w,
params["weight_scale"],
weight_scale,
b,
int8_convrot ? int8_convrot_group_size : 0,
scale);
@@ -3517,6 +3521,30 @@ public:
}
return out;
}
if (has_weight_scale) {
out = ggml_ext_linear(ctx->ggml_ctx, x, w, nullptr, force_prec_f32, scale);
out = ggml_mul(ctx->ggml_ctx, out, weight_scale);
if (ctx->weight_adapter) {
WeightAdapter::ForwardParams forward_params;
forward_params.op_type = WeightAdapter::ForwardParams::op_type_t::OP_LINEAR;
forward_params.linear.force_prec_f32 = force_prec_f32;
forward_params.linear.scale = scale;
out = ctx->weight_adapter->add_lora_to_output(ctx->ggml_ctx,
ctx->backend,
x,
w,
out,
prefix,
forward_params);
if (b != nullptr) {
b = ctx->weight_adapter->patch_weight(ctx->ggml_ctx, ctx->backend, b, prefix + "bias");
}
}
if (b != nullptr) {
out = ggml_add_inplace(ctx->ggml_ctx, out, b);
}
return out;
}
if (ctx->weight_adapter) {
WeightAdapter::ForwardParams forward_params;
forward_params.op_type = WeightAdapter::ForwardParams::op_type_t::OP_LINEAR;
@@ -3526,12 +3554,6 @@ public:
} else {
out = ggml_ext_linear(ctx->ggml_ctx, x, w, linear_bias, force_prec_f32, scale);
}
if (has_weight_scale) {
out = ggml_mul(ctx->ggml_ctx, out, params["weight_scale"]);
if (b != nullptr) {
out = ggml_add_inplace(ctx->ggml_ctx, out, b);
}
}
return out;
}
};