alphard/stable-diffusion.cpp
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use new graph api to avoid stack overflow on msvc

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leejet 2023-09-03 22:56:33 +08:00
parent 3ffffa6929
commit 7620b920c8
1 changed files with 26 additions and 26 deletions
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52
stable-diffusion.cpp
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@ -14,9 +14,9 @@
#include <vector>
#include "ggml/ggml.h"
#include "stable-diffusion.h"
#include "rng.h"
#include "rng_philox.h"
#include "stable-diffusion.h"
static SDLogLevel log_level = SDLogLevel::INFO;
@ -3122,8 +3122,8 @@ class StableDiffusionGGML {
struct ggml_tensor* out = diffusion_model.forward(ctx, x_t, NULL, c, t_emb);
ctx_size += ggml_used_mem(ctx) + ggml_used_mem_of_data(ctx);
struct ggml_cgraph diffusion_graph = ggml_build_forward(out);
struct ggml_cplan cplan = ggml_graph_plan(&diffusion_graph, n_threads);
struct ggml_cgraph* diffusion_graph = ggml_build_forward_ctx(ctx, out);
struct ggml_cplan cplan = ggml_graph_plan(diffusion_graph, n_threads);
ctx_size += cplan.work_size;
LOG_DEBUG("diffusion context need %.2fMB static memory, with work_size needing %.2fMB",
@ -3155,8 +3155,8 @@ class StableDiffusionGGML {
struct ggml_tensor* out = diffusion_model.forward(ctx, x_t, NULL, c, t_emb);
ggml_hold_dynamic_tensor(out);
struct ggml_cgraph diffusion_graph = ggml_build_forward(out);
struct ggml_cplan cplan = ggml_graph_plan(&diffusion_graph, n_threads);
struct ggml_cgraph* diffusion_graph = ggml_build_forward_ctx(ctx, out);
struct ggml_cplan cplan = ggml_graph_plan(diffusion_graph, n_threads);
ggml_set_dynamic(ctx, false);
struct ggml_tensor* buf = ggml_new_tensor_1d(ctx, GGML_TYPE_I8, cplan.work_size);
@ -3165,7 +3165,7 @@ class StableDiffusionGGML {
cplan.work_data = (uint8_t*)buf->data;
int64_t t0 = ggml_time_ms();
ggml_graph_compute(&diffusion_graph, &cplan);
ggml_graph_compute(diffusion_graph, &cplan);
double result = 0.f;
@ -3222,8 +3222,8 @@ class StableDiffusionGGML {
struct ggml_tensor* hidden_states = cond_stage_model.text_model.forward(ctx, input_ids);
struct ggml_cgraph cond_graph = ggml_build_forward(hidden_states);
struct ggml_cplan cplan = ggml_graph_plan(&cond_graph, n_threads);
struct ggml_cgraph* cond_graph = ggml_build_forward_ctx(ctx, hidden_states);
struct ggml_cplan cplan = ggml_graph_plan(cond_graph, n_threads);
ctx_size += cplan.work_size;
ctx_size += ggml_used_mem(ctx) + ggml_used_mem_of_data(ctx);
@ -3251,14 +3251,14 @@ class StableDiffusionGGML {
ggml_set_dynamic(ctx, params.dynamic);
struct ggml_tensor* hidden_states = cond_stage_model.text_model.forward(ctx, input_ids);
struct ggml_cgraph cond_graph = ggml_build_forward(hidden_states);
struct ggml_cgraph* cond_graph = ggml_build_forward_ctx(ctx, hidden_states);
LOG_DEBUG("building condition graph completed: %d nodes, %d leafs",
cond_graph.n_nodes, cond_graph.n_leafs);
cond_graph->n_nodes, cond_graph->n_leafs);
memcpy(input_ids->data, tokens.data(), tokens.size() * ggml_element_size(input_ids));
int64_t t0 = ggml_time_ms();
ggml_graph_compute_with_ctx(ctx, &cond_graph, n_threads);
ggml_graph_compute_with_ctx(ctx, cond_graph, n_threads);
int64_t t1 = ggml_time_ms();
LOG_DEBUG("computing condition graph completed, taking %.2fs", (t1 - t0) * 1.0f / 1000);
@ -3360,8 +3360,8 @@ class StableDiffusionGGML {
struct ggml_tensor* out = diffusion_model.forward(ctx, noised_input, NULL, context, t_emb);
ctx_size += ggml_used_mem(ctx) + ggml_used_mem_of_data(ctx);
struct ggml_cgraph diffusion_graph = ggml_build_forward(out);
struct ggml_cplan cplan = ggml_graph_plan(&diffusion_graph, n_threads);
struct ggml_cgraph* diffusion_graph = ggml_build_forward_ctx(ctx, out);
struct ggml_cplan cplan = ggml_graph_plan(diffusion_graph, n_threads);
ctx_size += cplan.work_size;
LOG_DEBUG("diffusion context need %.2fMB static memory, with work_size needing %.2fMB",
@ -3393,8 +3393,8 @@ class StableDiffusionGGML {
struct ggml_tensor* out = diffusion_model.forward(ctx, noised_input, NULL, context, t_emb);
ggml_hold_dynamic_tensor(out);
struct ggml_cgraph diffusion_graph = ggml_build_forward(out);
struct ggml_cplan cplan = ggml_graph_plan(&diffusion_graph, n_threads);
struct ggml_cgraph* diffusion_graph = ggml_build_forward_ctx(ctx, out);
struct ggml_cplan cplan = ggml_graph_plan(diffusion_graph, n_threads);
ggml_set_dynamic(ctx, false);
struct ggml_tensor* buf = ggml_new_tensor_1d(ctx, GGML_TYPE_I8, cplan.work_size);
@ -3452,12 +3452,12 @@ class StableDiffusionGGML {
if (cfg_scale != 1.0 && uc != NULL) {
// uncond
copy_ggml_tensor(context, uc);
ggml_graph_compute(&diffusion_graph, &cplan);
ggml_graph_compute(diffusion_graph, &cplan);
copy_ggml_tensor(out_uncond, out);
// cond
copy_ggml_tensor(context, c);
ggml_graph_compute(&diffusion_graph, &cplan);
ggml_graph_compute(diffusion_graph, &cplan);
out_cond = out;
@ -3474,7 +3474,7 @@ class StableDiffusionGGML {
} else {
// cond
copy_ggml_tensor(context, c);
ggml_graph_compute(&diffusion_graph, &cplan);
ggml_graph_compute(diffusion_graph, &cplan);
}
// v = out, eps = out
@ -3607,8 +3607,8 @@ class StableDiffusionGGML {
struct ggml_tensor* moments = first_stage_model.encode(ctx, x);
ctx_size += ggml_used_mem(ctx) + ggml_used_mem_of_data(ctx);
struct ggml_cgraph vae_graph = ggml_build_forward(moments);
struct ggml_cplan cplan = ggml_graph_plan(&vae_graph, n_threads);
struct ggml_cgraph* vae_graph = ggml_build_forward_ctx(ctx, moments);
struct ggml_cplan cplan = ggml_graph_plan(vae_graph, n_threads);
ctx_size += cplan.work_size;
LOG_DEBUG("vae context need %.2fMB static memory, with work_size needing %.2fMB",
@ -3632,10 +3632,10 @@ class StableDiffusionGGML {
}
struct ggml_tensor* moments = first_stage_model.encode(ctx, x);
struct ggml_cgraph vae_graph = ggml_build_forward(moments);
struct ggml_cgraph* vae_graph = ggml_build_forward_ctx(ctx, moments);
int64_t t0 = ggml_time_ms();
ggml_graph_compute_with_ctx(ctx, &vae_graph, n_threads);
ggml_graph_compute_with_ctx(ctx, vae_graph, n_threads);
int64_t t1 = ggml_time_ms();
#ifdef GGML_PERF
@ -3736,8 +3736,8 @@ class StableDiffusionGGML {
struct ggml_tensor* img = first_stage_model.decoder.forward(ctx, z);
ctx_size += ggml_used_mem(ctx) + ggml_used_mem_of_data(ctx);
struct ggml_cgraph vae_graph = ggml_build_forward(img);
struct ggml_cplan cplan = ggml_graph_plan(&vae_graph, n_threads);
struct ggml_cgraph* vae_graph = ggml_build_forward_ctx(ctx, img);
struct ggml_cplan cplan = ggml_graph_plan(vae_graph, n_threads);
ctx_size += cplan.work_size;
LOG_DEBUG("vae context need %.2fMB static memory, with work_size needing %.2fMB",
@ -3761,10 +3761,10 @@ class StableDiffusionGGML {
}
struct ggml_tensor* img = first_stage_model.decode(ctx, z);
struct ggml_cgraph vae_graph = ggml_build_forward(img);
struct ggml_cgraph* vae_graph = ggml_build_forward_ctx(ctx, img);
int64_t t0 = ggml_time_ms();
ggml_graph_compute_with_ctx(ctx, &vae_graph, n_threads);
ggml_graph_compute_with_ctx(ctx, vae_graph, n_threads);
int64_t t1 = ggml_time_ms();
#ifdef GGML_PERF