mirror of
https://github.com/leejet/stable-diffusion.cpp.git
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251 lines
9.9 KiB
C++
251 lines
9.9 KiB
C++
#ifndef __SD_CORE_GGML_EXTEND_H__
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#define __SD_CORE_GGML_EXTEND_H__
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#include <cstdint>
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#include <vector>
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#include "ggml-backend.h"
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#include "ggml.h"
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#define EPS 1e-05f
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static_assert(GGML_MAX_NAME >= 160, "GGML_MAX_NAME must be at least 160");
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// n-mode tensor-matrix product
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// example: 2-mode product
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// A: [ne03, k, ne01, ne00]
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// B: k rows, m columns => [k, m]
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// result is [ne03, m, ne01, ne00]
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ggml_tensor* ggml_ext_mul_n_mode(ggml_context* ctx, ggml_tensor* a, ggml_tensor* b, int mode = 0);
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// Kronecker product
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// [ne03,ne02,ne01,ne00] x [ne13,ne12,ne11,ne10] => [ne03*ne13,ne02*ne12,ne01*ne11,ne00*ne10]
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ggml_tensor* ggml_ext_kronecker(ggml_context* ctx, ggml_tensor* a, ggml_tensor* b);
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ggml_tensor* ggml_ext_cont(ggml_context* ctx,
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ggml_tensor* x);
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// torch like permute
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ggml_tensor* ggml_ext_torch_permute(ggml_context* ctx,
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ggml_tensor* x,
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int axis0,
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int axis1,
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int axis2,
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int axis3);
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ggml_tensor* ggml_ext_slice(ggml_context* ctx,
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ggml_tensor* x,
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int dim,
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int64_t start,
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int64_t end,
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bool cont = true);
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// example: [N, 3*C, H, W] => ([N, C, H, W], [N, C, H, W], [N, C, H, W])
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std::vector<ggml_tensor*> ggml_ext_chunk(ggml_context* ctx,
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ggml_tensor* x,
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int num,
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int64_t dim,
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bool cont = true);
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ggml_tensor* ggml_ext_silu_act(ggml_context* ctx, ggml_tensor* x, bool gate_first = true);
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ggml_tensor* ggml_ext_group_norm_32(ggml_context* ctx,
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ggml_tensor* a);
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ggml_tensor* ggml_ext_scale(ggml_context* ctx,
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ggml_tensor* x,
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float factor,
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bool inplace = false);
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ggml_tensor* ggml_ext_gelu(ggml_context* ctx,
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ggml_tensor* x,
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bool inplace = false);
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ggml_tensor* ggml_ext_gelu_quick(ggml_context* ctx,
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ggml_tensor* x,
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bool inplace = false);
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ggml_tensor* ggml_ext_linear(ggml_context* ctx,
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ggml_tensor* x,
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ggml_tensor* w,
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ggml_tensor* b,
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bool force_prec_f32 = false,
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float scale = 1.f);
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ggml_tensor* ggml_ext_linear_i8_tensorwise(ggml_context* ctx,
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ggml_tensor* x,
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ggml_tensor* w,
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ggml_tensor* weight_scale,
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ggml_tensor* b,
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int convrot_group_size,
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float scale = 1.f);
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ggml_tensor* ggml_ext_pad_ext(ggml_context* ctx,
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ggml_backend_t backend,
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ggml_tensor* x,
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int lp0,
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int rp0,
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int lp1,
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int rp1,
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int lp2,
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int rp2,
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int lp3,
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int rp3,
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bool circular_x = false,
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bool circular_y = false);
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ggml_tensor* ggml_ext_pad(ggml_context* ctx,
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ggml_tensor* x,
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int p0,
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int p1,
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int p2 = 0,
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int p3 = 0,
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bool circular_x = false,
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bool circular_y = false);
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// ggml layout: x [L, IC, N], w [K, IC/groups, OC], b [OC], result [OL, OC, N].
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// force_prec_f32 keeps both input patches and weights in F32.
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ggml_tensor* ggml_ext_conv_1d(ggml_context* ctx,
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ggml_tensor* x,
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ggml_tensor* w,
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ggml_tensor* b,
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int s0 = 1,
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int p0 = 0,
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int d0 = 1,
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int64_t groups = 1,
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bool force_prec_f32 = false);
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// w: [OC，IC, KH, KW]
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// x: [N, IC, IH, IW]
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// b: [OC,]
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// result: [N, OC, OH, OW]
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ggml_tensor* ggml_ext_conv_2d(ggml_context* ctx,
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ggml_tensor* x,
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ggml_tensor* w,
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ggml_tensor* b,
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int s0 = 1,
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int s1 = 1,
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int p0 = 0,
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int p1 = 0,
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int d0 = 1,
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int d1 = 1,
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bool direct = false,
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bool circular_x = false,
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bool circular_y = false,
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float scale = 1.f);
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// w: [OC，IC, KD, 1 * 1]
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// x: [N, IC, IH, IW]
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// b: [OC,]
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// result: [N*OC, OD, OH, OW]
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ggml_tensor* ggml_ext_conv_3d(ggml_context* ctx,
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ggml_backend_t backend,
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ggml_tensor* x,
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ggml_tensor* w,
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ggml_tensor* b,
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int64_t IC,
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int s0 = 1,
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int s1 = 1,
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int s2 = 1,
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int p0 = 0,
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int p1 = 0,
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int p2 = 0,
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int d0 = 1,
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int d1 = 1,
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int d2 = 1,
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bool force_prec_f32 = false,
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bool direct = false);
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// w: [OC，IC, KD, 1 * 1]
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// x: [N, IC, ID, IH*IW]
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// b: [OC,]
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// result: [N, OC, OD, OH*OW]
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ggml_tensor* ggml_ext_conv_3d_nx1x1(ggml_context* ctx,
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ggml_tensor* x,
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ggml_tensor* w,
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ggml_tensor* b,
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int s2 = 1,
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int p2 = 1,
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int d2 = 1);
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// qkv: [N, L, 3*C]
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// return: ([N, L, C], [N, L, C], [N, L, C])
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std::vector<ggml_tensor*> split_qkv(ggml_context* ctx,
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ggml_tensor* qkv);
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// qkv: [N, 3*C, H, W]
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// return: ([N, C, H, W], [N, C, H, W], [N, C, H, W])
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std::vector<ggml_tensor*> split_image_qkv(ggml_context* ctx,
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ggml_tensor* qkv);
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// Constant and cast helpers require the built-in tensors initialized by GGMLRunner.
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ggml_tensor* ggml_ext_full(ggml_context* ctx,
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float value,
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int64_t ne0,
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int64_t ne1,
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int64_t ne2,
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int64_t ne3);
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ggml_tensor* ggml_ext_zeros(ggml_context* ctx,
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int64_t ne0,
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int64_t ne1,
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int64_t ne2,
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int64_t ne3);
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ggml_tensor* ggml_ext_zeros_like(ggml_context* ctx,
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ggml_tensor* x);
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ggml_tensor* ggml_ext_ones(ggml_context* ctx,
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int64_t ne0,
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int64_t ne1,
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int64_t ne2,
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int64_t ne3);
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ggml_tensor* ggml_ext_ones_like(ggml_context* ctx,
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ggml_tensor* x);
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ggml_tensor* ggml_ext_cast_f32(ggml_context* ctx, ggml_backend_t backend, ggml_tensor* a);
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// q: [N, L_q, C(n_head*d_head)] or [N*n_head, L_q, d_head]
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// k: [N, L_k, n_kv_head*d_head] or [N*n_kv_head, L_k, d_head]
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// v: [N, L_k, n_kv_head*d_head] or [N, L_k, n_kv_head, d_head]
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// mask: [N, L_q, L_k]
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// return: [N, L_q, C]
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ggml_tensor* ggml_ext_attention_ext(ggml_context* ctx,
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ggml_backend_t backend,
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ggml_tensor* q,
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ggml_tensor* k,
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ggml_tensor* v,
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int64_t n_head,
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ggml_tensor* mask = nullptr,
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bool skip_reshape = false,
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bool flash_attn = false,
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float kv_scale = 1.0f,
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bool sage_attn = false);
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ggml_tensor* ggml_ext_layer_norm(ggml_context* ctx,
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ggml_tensor* x,
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ggml_tensor* w,
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ggml_tensor* b,
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float eps = EPS);
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ggml_tensor* ggml_ext_group_norm(ggml_context* ctx,
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ggml_tensor* x,
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ggml_tensor* w,
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ggml_tensor* b,
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int num_groups = 32,
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float eps = 1e-6f);
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ggml_tensor* ggml_ext_timestep_embedding(
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ggml_context* ctx,
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ggml_tensor* timesteps,
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int dim,
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int max_period = 10000,
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float time_factor = 1.0f);
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ggml_tensor* ggml_ext_vec_concat(ggml_context* ctx,
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std::vector<ggml_tensor*>& tensors,
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int dim);
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#endif // __SD_CORE_GGML_EXTEND_H__
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