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clip : bring back GPU support #12322

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merged 5 commits into from
Mar 11, 2025
Merged

clip : bring back GPU support #12322

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bf1d72b
clip : bring back GPU support
ngxson Mar 10, 2025
9b83493
use n_gpu_layers param
ngxson Mar 10, 2025
290a82d
fix double free
ngxson Mar 10, 2025
3e45ea4
ggml_backend_init_by_type
ngxson Mar 10, 2025
d95c01a
clean up
ngxson Mar 10, 2025
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149 changes: 75 additions & 74 deletions examples/llava/clip.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -4,31 +4,12 @@
// Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
#include "clip.h"
#include "ggml.h"
#include "ggml-cpp.h"
#include "ggml-cpu.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "gguf.h"

//#ifdef GGML_USE_CUDA
//#include "ggml-cuda.h"
//#endif
//
//#ifdef GGML_USE_SYCL
//#include "ggml-sycl.h"
//#endif
//
//#ifdef GGML_USE_METAL
//#include "ggml-metal.h"
//#endif
//
//#ifdef GGML_USE_CANN
//#include "ggml-cann.h"
//#endif
//
//#ifdef GGML_USE_VULKAN
//#include "ggml-vulkan.h"
//#endif

#define STB_IMAGE_IMPLEMENTATION
#include "stb_image.h"

Expand Down Expand Up @@ -600,18 +581,54 @@ struct clip_ctx {
bool has_post_norm = false;
bool has_patch_bias = false;

struct gguf_context * ctx_gguf;
struct ggml_context * ctx_data;
struct gguf_context * ctx_gguf = nullptr;
struct ggml_context * ctx_data = nullptr;

std::vector<uint8_t> buf_compute_meta;

// memory buffers to evaluate the model
ggml_backend_buffer_t params_buffer = NULL;
std::vector<ggml_backend_t> backend_ptrs;
std::vector<ggml_backend_buffer_type_t> backend_buft;

ggml_backend_t backend = nullptr;
ggml_backend_t backend_cpu = nullptr;
ggml_backend_buffer_t buf = nullptr;

ggml_backend_t backend = NULL;
ggml_gallocr_t compute_alloc = NULL;
ggml_backend_sched_ptr sched;

struct clip_image_size * load_image_size;

clip_ctx(clip_context_params & ctx_params) {
backend_cpu = ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_CPU, nullptr);
backend = ctx_params.use_gpu
? ggml_backend_init_by_type(GGML_BACKEND_DEVICE_TYPE_GPU, nullptr)
: nullptr;

if (backend) {
LOG_INF("%s: CLIP using %s backend\n", __func__, ggml_backend_name(backend));
backend_ptrs.push_back(backend);
backend_buft.push_back(ggml_backend_get_default_buffer_type(backend));
} else {
backend = backend_cpu;
LOG_INF("%s: CLIP using CPU backend\n", __func__);
}

backend_ptrs.push_back(backend_cpu);
backend_buft.push_back(ggml_backend_get_default_buffer_type(backend_cpu));

sched.reset(
ggml_backend_sched_new(backend_ptrs.data(), backend_buft.data(), backend_ptrs.size(), 8192, false)
);
}

~clip_ctx() {
ggml_free(ctx_data);
gguf_free(ctx_gguf);
ggml_backend_buffer_free(buf);
ggml_backend_free(backend);
if (backend_cpu != backend) {
ggml_backend_free(backend_cpu);
}
}
};

static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch * imgs, struct clip_image_size * load_image_size, bool is_inf = false) {
Expand Down Expand Up @@ -1184,6 +1201,14 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32

// read and create ggml_context containing the tensors and their data
struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
return clip_init(fname, clip_context_params{
/* use_gpu */ true,
/* verbosity */ verbosity,
});
}

struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_params) {
int verbosity = ctx_params.verbosity;
struct ggml_context * meta = NULL;

struct gguf_init_params params = {
Expand Down Expand Up @@ -1277,7 +1302,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
}
}

clip_ctx * new_clip = new clip_ctx{};
clip_ctx * new_clip = new clip_ctx(ctx_params);

// update projector type
{
Expand All @@ -1296,36 +1321,6 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
}
}

//#ifdef GGML_USE_CUDA
// new_clip->backend = ggml_backend_cuda_init(0);
// LOG_INF("%s: CLIP using CUDA backend\n", __func__);
//#endif
//
//#ifdef GGML_USE_METAL
// new_clip->backend = ggml_backend_metal_init();
// LOG_INF("%s: CLIP using Metal backend\n", __func__);
//#endif
//
//#ifdef GGML_USE_CANN
// new_clip->backend = ggml_backend_cann_init(0);
// LOG_INF("%s: CLIP using CANN backend\n", __func__);
//#endif
//
//#ifdef GGML_USE_VULKAN
// new_clip->backend = ggml_backend_vk_init(0);
// LOG_INF("%s: CLIP using Vulkan backend\n", __func__);
//#endif
//
//#ifdef GGML_USE_SYCL
// new_clip->backend = ggml_backend_sycl_init(0);
// LOG_INF("%s: CLIP using SYCL backend\n", __func__);
//#endif

if (!new_clip->backend) {
new_clip->backend = ggml_backend_cpu_init();
LOG_INF("%s: CLIP using CPU backend\n", __func__);
}

// model size and capabilities
{
int idx = get_key_idx(ctx, KEY_HAS_TEXT_ENC);
Expand Down Expand Up @@ -1421,7 +1416,9 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
}

// alloc memory and offload data
new_clip->params_buffer = ggml_backend_alloc_ctx_tensors(new_clip->ctx_data, new_clip->backend);
ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(new_clip->backend);
new_clip->buf = ggml_backend_alloc_ctx_tensors_from_buft(new_clip->ctx_data, buft);
ggml_backend_buffer_set_usage(new_clip->buf, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
for (int i = 0; i < n_tensors; ++i) {
const char * name = gguf_get_tensor_name(ctx, i);
struct ggml_tensor * cur = ggml_get_tensor(new_clip->ctx_data, name);
Expand All @@ -1434,7 +1431,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
return nullptr;
}
int num_bytes = ggml_nbytes(cur);
if (ggml_backend_buffer_is_host(new_clip->params_buffer)) {
if (ggml_backend_buft_is_host(buft)) {
// for the CPU and Metal backend, we can read directly into the tensor
fin.read(reinterpret_cast<char *>(cur->data), num_bytes);
} else {
Expand Down Expand Up @@ -1720,14 +1717,21 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
// measure mem requirement and allocate
{
new_clip->buf_compute_meta.resize(GGML_DEFAULT_GRAPH_SIZE * ggml_tensor_overhead() + ggml_graph_overhead());
new_clip->compute_alloc = ggml_gallocr_new(ggml_backend_get_default_buffer_type(new_clip->backend));
clip_image_f32_batch batch;
batch.size = 1;
batch.data = nullptr;
ggml_cgraph * gf = clip_image_build_graph(new_clip, &batch, nullptr, false);
ggml_gallocr_reserve(new_clip->compute_alloc, gf);
size_t compute_memory_buffer_size = ggml_gallocr_get_buffer_size(new_clip->compute_alloc, 0);
LOG_INF("%s: compute allocated memory: %.2f MB\n", __func__, compute_memory_buffer_size /1024.0/1024.0);
ggml_backend_sched_reserve(new_clip->sched.get(), gf);
for (size_t i = 0; i < new_clip->backend_ptrs.size(); ++i) {
ggml_backend_t backend = new_clip->backend_ptrs[i];
ggml_backend_buffer_type_t buft = new_clip->backend_buft[i];
size_t size = ggml_backend_sched_get_buffer_size(new_clip->sched.get(), backend);
if (size > 1) {
LOG_INF("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
ggml_backend_buft_name(buft),
size / 1024.0 / 1024.0);
}
}
}

return new_clip;
Expand Down Expand Up @@ -2408,12 +2412,6 @@ ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx) {
}

void clip_free(clip_ctx * ctx) {
ggml_free(ctx->ctx_data);
gguf_free(ctx->ctx_gguf);

ggml_backend_buffer_free(ctx->params_buffer);
ggml_backend_free(ctx->backend);
ggml_gallocr_free(ctx->compute_alloc);
delete ctx;
}

Expand Down Expand Up @@ -2609,8 +2607,9 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
}

// build the inference graph
ggml_backend_sched_reset(ctx->sched.get());
ggml_cgraph * gf = clip_image_build_graph(ctx, imgs, ctx->load_image_size, true);
ggml_gallocr_alloc_graph(ctx->compute_alloc, gf);
ggml_backend_sched_alloc_graph(ctx->sched.get(), gf);

// set inputs
const auto & model = ctx->vision_model;
Expand Down Expand Up @@ -2775,11 +2774,13 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
}
}

if (ggml_backend_is_cpu(ctx->backend)) {
ggml_backend_cpu_set_n_threads(ctx->backend, n_threads);
}
ggml_backend_cpu_set_n_threads(ctx->backend_cpu, n_threads);

ggml_backend_graph_compute(ctx->backend, gf);
auto status = ggml_backend_sched_graph_compute(ctx->sched.get(), gf);
if (status != GGML_STATUS_SUCCESS) {
LOG_ERR("%s: ggml_backend_sched_graph_compute failed with error %d\n", __func__, status);
return false;
}

// the last node is the embedding tensor
struct ggml_tensor * embeddings = ggml_graph_node(gf, -1);
Expand Down
11 changes: 9 additions & 2 deletions examples/llava/clip.h
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Original file line number Diff line number Diff line change
Expand Up @@ -39,8 +39,15 @@ struct clip_image_f32_batch {
size_t size;
};

CLIP_API struct clip_ctx * clip_model_load (const char * fname, int verbosity);
CLIP_API struct clip_ctx * clip_model_load_cpu(const char * fname, int verbosity);
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Please note that clip_model_load_cpu has no implementation, so I removed it

struct clip_context_params {
bool use_gpu;
int verbosity;
};

// deprecated, use clip_init
CLIP_API struct clip_ctx * clip_model_load(const char * fname, int verbosity);

CLIP_API struct clip_ctx * clip_init(const char * fname, struct clip_context_params ctx_params);

CLIP_API void clip_free(struct clip_ctx * ctx);

Expand Down
6 changes: 5 additions & 1 deletion examples/llava/minicpmv-cli.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -86,7 +86,11 @@ static struct clip_ctx * clip_init_context(common_params * params) {
if (prompt.empty()) {
prompt = "describe the image in detail.";
}
auto * ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
struct clip_context_params clip_params = {
/* use_gpu */ params->n_gpu_layers != 0,
/* verbosity */ params->verbosity,
};
auto * ctx_clip = clip_init(clip_path, clip_params);
return ctx_clip;
}

Expand Down
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