vrr/prima.cpp
2
0
Fork 0
mirror of https://github.com/Lizonghang/prima.cpp.git synced 2025-09-05 23:39:05 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

fix flops count and ram/vram speed test

Browse source
This commit is contained in:
Zonghang Li 2024-12-08 10:14:05 +04:00
parent 26c2ffb5b7
commit df813675d0
5 changed files with 136 additions and 83 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

3
common/common.cpp
View file

@ -896,7 +896,7 @@ struct llama_init_result llama_init_from_gpt_params(gpt_params & params) {
device_info dev_info;
dev_info.rank = params.rank;
llama_profile_device(&dev_info, model, ml, params.n_predict, params.cpuparams.n_threads);
llama_profile_device(&dev_info, model, ml, params.n_predict, params.n_ctx, params.cpuparams.n_threads, params.flash_attn);
// create llama context
struct llama_context_params cparams = llama_context_params_from_gpt_params(params);
@ -1133,6 +1133,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
std::strcpy(cparams.next_node_ip, params.next_node_ip.c_str());
cparams.n_ctx = params.n_ctx;
cparams.n_predict = params.n_predict;
cparams.n_seq_max = params.n_parallel;
cparams.n_batch = params.n_batch;
cparams.n_ubatch = params.n_ubatch;

67
common/profiler.cpp
View file

@ -97,8 +97,9 @@ uint32_t device_cpu_cores() {
}
static float device_flops(struct llama_model * model, enum ggml_type src0t, enum ggml_type src1t, enum profiler_backend_type btype, int n_threads) {
const int n_repeat = 1;
const int n_embd = std::min(llama_n_embd(model), 4096);
int n_repeat = 1;
int n_embd = std::min(llama_n_embd(model), 4096);
if (btype == PROFILER_BACKEND_TYPE_CPU) n_embd /= 8; // simulate small tensor calculation on cpu
std::vector<float> matrix_A(n_embd * n_embd, 1.0f);
std::vector<float> matrix_B(n_embd * n_embd, 1.0f / n_embd);
@ -142,12 +143,9 @@ static float device_flops(struct llama_model * model, enum ggml_type src0t, enum
struct ggml_cgraph * gf = NULL;
struct ggml_context * ctx_cgraph = NULL;
struct ggml_tensor * cur = NULL;
struct ggml_tensor * cur1 = NULL;
struct ggml_tensor * cur2 = NULL;
struct ggml_tensor * cur3 = NULL;
{
struct ggml_init_params params0 = {
/*.mem_size =*/ ggml_tensor_overhead() * (5 * n_repeat + 1) + ggml_graph_overhead(),
/*.mem_size =*/ ggml_tensor_overhead() * (n_repeat + 2) + ggml_graph_overhead(),
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
};
@ -155,12 +153,8 @@ static float device_flops(struct llama_model * model, enum ggml_type src0t, enum
gf = ggml_new_graph(ctx_cgraph);
cur = ggml_mul_mat(ctx_cgraph, tensor_a, tensor_b);
for (int i = 0; i < n_repeat; i++) {
cur1 = ggml_mul_mat(ctx_cgraph, tensor_a, cur);
cur2 = ggml_mul_mat(ctx_cgraph, tensor_a, cur);
cur = ggml_add(ctx_cgraph, cur1, cur2);
cur3 = ggml_mul_mat(ctx_cgraph, tensor_a, cur);
cur = ggml_add(ctx_cgraph, cur, cur3);
for (int i = 0; i < n_repeat - 1; i++) {
cur = ggml_mul_mat(ctx_cgraph, tensor_a, cur);
}
ggml_build_forward_expand(gf, cur);
}
@ -204,15 +198,14 @@ static float device_flops(struct llama_model * model, enum ggml_type src0t, enum
ggml_backend_sched_alloc_graph(sched, gf);
// warm-up
// ggml_backend_graph_compute(backend, gf);
ggml_backend_graph_compute(backend, gf);
const int64_t t_start = ggml_time_us();
ggml_backend_graph_compute(backend, gf);
const int64_t t_end = ggml_time_us();
double elapsed_seconds = ((double)t_end - (double)t_start) / 1e6; // convert to seconds
double flops = (2.0 * (double)n_embd * (double)n_embd * (double)n_embd +
n_repeat * 4 * 2.0 * (double)n_embd * (double)n_embd * (double)n_embd) / elapsed_seconds / 1e9; // convert to GFLOPS
double flops = (2.0 * (double)n_embd * (double)n_embd * (double)n_embd * n_repeat) / elapsed_seconds / 1e9; // convert to GFLOPS
ggml_free(ctx_cgraph);
ggml_gallocr_free(allocr);
@ -933,8 +926,8 @@ float device_memory_bw(int n_thread) {
return static_cast<float>(bandwidth);
}
static float device_read_vram_bw(struct llama_model * model, enum profiler_backend_type btype) {
const int n_embd = std::min(llama_n_embd(model) * 2, 4096 * 2);
static float device_read_vram_bw(enum profiler_backend_type btype) {
const int n_embd = 8192;
std::vector<float> matrix_A(n_embd * n_embd, 1.0f);
ggml_backend_t backend = NULL;
@ -1006,21 +999,19 @@ static float device_read_vram_bw(struct llama_model * model, enum profiler_backe
return bandwidth;
}
float device_metal_read_vram_bw(struct llama_model * model) {
float device_metal_read_vram_bw() {
#ifdef GGML_USE_METAL
return device_read_vram_bw(model, PROFILER_BACKEND_TYPE_METAL);
return device_read_vram_bw(PROFILER_BACKEND_TYPE_METAL);
#endif
(void)model;
return 0.0f;
}
float device_cuda_read_vram_bw(struct llama_model * model) {
float device_cuda_read_vram_bw() {
#ifdef GGML_USE_CUDA
return device_read_vram_bw(model, PROFILER_BACKEND_TYPE_CUDA);
return device_read_vram_bw(PROFILER_BACKEND_TYPE_CUDA);
#endif
(void)model;
return 0.0f;
}
@ -1124,7 +1115,7 @@ static float device_compute_delay(struct device_info & dev_info, int n_layers, c
}
// estimate the memory access delay, except for the input embedding because it has been considered in n_flops.inp_embd_ms
static float device_memory_access_delay(struct device_info & dev_info, const struct llama_context_params cparams, int n_layers) {
static float device_memory_access_delay(struct device_info & dev_info, struct llama_model * model, const struct llama_context_params cparams, int n_layers) {
struct model_params n_params = dev_info.model_params;
int n_gpu_layers = std::min(static_cast<int>(cparams.n_gpu_layers), n_layers);
@ -1143,10 +1134,15 @@ static float device_memory_access_delay(struct device_info & dev_info, const str
n_params.output_q5k * 5 / 8 +
n_params.output_q6k * 6 / 8 +
n_params.output_q80;
uint64_t cpu_kv_size;
uint64_t gpu_kv_size;
#if defined(GGML_USE_CUDA) || defined(GGML_USE_METAL)
int64_t vram_bytes = layer_bytes * n_gpu_layers;
int64_t ram_bytes = layer_bytes * (n_layers - n_gpu_layers) + output_bytes;
#if defined(GGML_USE_METAL) || defined(GGML_USE_CUDA)
llama_kv_size(&cpu_kv_size, &gpu_kv_size, model, cparams, true);
int64_t vram_bytes = layer_bytes * n_gpu_layers + gpu_kv_size;
int64_t ram_bytes = layer_bytes * (n_layers - n_gpu_layers) + output_bytes + cpu_kv_size;
#ifdef GGML_USE_CUDA
double vram_access_delay = (double)(vram_bytes) / 1e6 / dev_info.gpu_props.cuda_read_vram_bw;
@ -1158,8 +1154,11 @@ static float device_memory_access_delay(struct device_info & dev_info, const str
return static_cast<float>(vram_access_delay + ram_access_delay); // ms
#else
llama_kv_size(&cpu_kv_size, &gpu_kv_size, model, cparams, false);
(void)n_gpu_layers;
int64_t ram_bytes = layer_bytes * n_layers + output_bytes;
(void)gpu_kv_size;
int64_t ram_bytes = layer_bytes * n_layers + output_bytes + cpu_kv_size;
double ram_access_delay = (double)(ram_bytes) / 1e6 / dev_info.memory.cpu_read_ram_bw;
return static_cast<float>(ram_access_delay); // ms
#endif
@ -1191,7 +1190,9 @@ static float device_disk_access_delay(struct device_info & dev_info, struct llam
#if defined(GGML_USE_METAL) || defined(GGML_USE_CUDA)
cpu_total_bytes += layer_bytes * (n_layers - n_gpu_layers);
#if defined(GGML_USE_METAL)
int64_t gpu_total_bytes = layer_bytes * n_gpu_layers;
#endif
#else
(void)n_gpu_layers;
cpu_total_bytes += layer_bytes * n_layers;
@ -1211,10 +1212,10 @@ static float device_disk_access_delay(struct device_info & dev_info, struct llam
uint64_t gpu_compute_buf;
#if defined(GGML_USE_METAL) || defined(GGML_USE_CUDA)
llama_model_kvcache_size(&cpu_kv_size, &gpu_kv_size, model, cparams, true);
llama_total_kv_size(&cpu_kv_size, &gpu_kv_size, model, cparams, true);
llama_model_compute_buf_size(&cpu_compute_buf, &gpu_compute_buf, model, cparams, true);
#else
llama_model_kvcache_size(&cpu_kv_size, &gpu_kv_size, model, cparams, false);
llama_total_kv_size(&cpu_kv_size, &gpu_kv_size, model, cparams, false);
llama_model_compute_buf_size(&cpu_compute_buf, &gpu_compute_buf, model, cparams, false);
#endif
@ -1651,9 +1652,9 @@ void device_print_props(struct device_info * dev_info_set, int n, struct llama_m
// todo: calculate for each device, not only master
float latency = 0.0f;
int n_layers = llama_model_n_layers (model);
latency += device_compute_delay (dev_info_set[0], n_layers, cparams);
latency += device_memory_access_delay(dev_info_set[0], cparams, n_layers);
latency += device_disk_access_delay (dev_info_set[0], model, cparams); // if physical memory is not enough, some mapped data will be released and reloaded later
latency += device_compute_delay (dev_info_set[0], n_layers,cparams);
latency += device_memory_access_delay(dev_info_set[0], model, cparams, n_layers);
latency += device_disk_access_delay (dev_info_set[0], model, cparams); // if physical memory is not enough, some mapped data will be released and reloaded later
LOG_INF("| Token latency (ms) ");
LOG_INF("| %-10.2f ", latency);

4
common/profiler.h
View file

@ -241,8 +241,8 @@ uint64_t device_swap_memory (bool available);
void device_disk_seq_bw (float * read_seq_bw, float * write_seq_bw, int n_threads);
void device_disk_rnd_bw (float * read_rnd_bw, float * write_rnd_bw, int n_threads);
float device_memory_bw (int n_thread);
float device_metal_read_vram_bw(struct llama_model * model);
float device_cuda_read_vram_bw (struct llama_model * model);
float device_metal_read_vram_bw();
float device_cuda_read_vram_bw ();
void device_get_props (struct llama_model * model, int device, struct ggml_backend_dev_props * props);
void device_print_props (struct device_info * dev_info_set, int n, struct llama_model * model, const struct llama_context_params cparams);

25
include/llama.h
View file

@ -325,6 +325,7 @@ extern "C" {
char * master_ip; // ip address of the master node
char * next_node_ip; // ip address of the next node
uint32_t n_ctx; // text context, 0 = from model
uint32_t n_predict; // number of tokens to predict
uint32_t n_batch; // logical maximum batch size that can be submitted to llama_decode
uint32_t n_ubatch; // physical maximum batch size
uint32_t n_seq_max; // max number of sequences (i.e. distinct states for recurrent models)
@ -412,11 +413,13 @@ extern "C" {
LLAMA_API void llama_backend_init(void);
LLAMA_API void llama_profile_device(
struct device_info * dev_info,
struct llama_model * model,
struct device_info * dev_info,
struct llama_model * model,
struct llama_model_loader * ml,
int n_predict,
int n_threads);
int n_predict,
int n_ctx,
int n_threads,
bool flash_attn);
LLAMA_API ggml_backend_buffer_type_t llama_dev_buffer_type(struct llama_model * model, int device);
@ -534,12 +537,19 @@ extern "C" {
bool use_gpu);
// Return the size of KV cache in the model
LLAMA_API void llama_model_kvcache_size(
LLAMA_API void llama_total_kv_size(
uint64_t * cpu_cache,
uint64_t * gpu_cache,
const struct llama_model * model,
const struct llama_context_params cparams,
bool use_gpu);
LLAMA_API void llama_kv_size(
uint64_t * cpu_cache,
uint64_t * gpu_cache,
const struct llama_model * model,
const struct llama_context_params cparams,
bool use_gpu);
// Return the total number of float operations in the model
LLAMA_API void llama_model_n_flops(
@ -547,9 +557,10 @@ extern "C" {
struct llama_model_loader * ml,
struct model_flops * n_flops,
struct model_params * n_params,
const int64_t n_input,
const int64_t n_history,
enum ggml_type * inp_embd_dtype);
const int64_t n_ctx,
enum ggml_type * inp_embd_dtype,
bool flash_attn);
// Get a llama model tensor
LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);

120
src/llama.cpp
View file

@ -3570,7 +3570,14 @@ static bool is_dtype_exist(struct model_params * n_params, enum ggml_type dtype)
}
}
void llama_profile_device(device_info * dev_info, struct llama_model * model, llama_model_loader * ml, int n_predict, int n_threads) {
void llama_profile_device(
device_info * dev_info,
struct llama_model * model,
llama_model_loader * ml,
int n_predict,
int n_ctx,
int n_threads,
bool flash_attn) {
dev_info->device_name = device_name();
dev_info->cpu_props.cores = device_cpu_cores();
@ -3584,7 +3591,7 @@ void llama_profile_device(device_info * dev_info, struct llama_model * model, ll
struct model_params * n_params = &dev_info->model_params;
if (dev_info->rank == 0) {
enum ggml_type inp_embd_dtype = GGML_TYPE_F32;
llama_model_n_flops(model, ml, n_flops, n_params, 1, n_predict, &inp_embd_dtype);
llama_model_n_flops(model, ml, n_flops, n_params, n_predict, n_ctx, &inp_embd_dtype, flash_attn);
n_flops->inp_embd_ms = device_inp_embd_delay(model, inp_embd_dtype, 1, n_threads);
}
@ -3611,8 +3618,8 @@ void llama_profile_device(device_info * dev_info, struct llama_model * model, ll
dev_info->gpu_props.description = gpu_props.description;
dev_info->gpu_props.memory_free = round(gpu_props.memory_free / (double)(1 << 30) * 100) / 100;
dev_info->gpu_props.memory_total = round(gpu_props.memory_total / (double)(1 << 30) * 100) / 100;
dev_info->gpu_props.metal_read_vram_bw = device_metal_read_vram_bw(model);
dev_info->gpu_props.cuda_read_vram_bw = device_cuda_read_vram_bw(model);
dev_info->gpu_props.metal_read_vram_bw = device_metal_read_vram_bw();
dev_info->gpu_props.cuda_read_vram_bw = device_cuda_read_vram_bw();
if (is_dtype_exist(n_params, GGML_TYPE_F32)) {
dev_info->cpu_props.flops_f32_f32 = device_cpu_flops (model, GGML_TYPE_F32, GGML_TYPE_F32, n_threads);
@ -19669,6 +19676,7 @@ struct llama_context_params llama_context_default_params() {
/*.master_ip =*/ nullptr,
/*.next_node_ip =*/ nullptr,
/*.n_ctx =*/ 512,
/*.n_predict =*/ 512,
/*.n_batch =*/ 2048,
/*.n_ubatch =*/ 512,
/*.n_seq_max =*/ 1,
@ -20910,22 +20918,49 @@ void llama_model_compute_buf_size(
}
}
void llama_model_kvcache_size(
void llama_total_kv_size(
uint64_t * cpu_cache,
uint64_t * gpu_cache,
const struct llama_model * model,
const struct llama_context_params cparams,
bool use_gpu) {
const llama_hparams hparams = model->hparams;
uint64_t ne_k = static_cast<uint64_t>(hparams.n_embd_k_gqa()) * cparams.n_ctx * ggml_type_size(cparams.type_k);
uint64_t ne_v = static_cast<uint64_t>(hparams.n_embd_v_gqa()) * cparams.n_ctx * ggml_type_size(cparams.type_v);
uint64_t nb_k = static_cast<uint64_t>(hparams.n_embd_k_gqa()) * cparams.n_ctx * ggml_type_size(cparams.type_k);
uint64_t nb_v = static_cast<uint64_t>(hparams.n_embd_v_gqa()) * cparams.n_ctx * ggml_type_size(cparams.type_v);
if (use_gpu) {
int n_gpu_layers = std::min(cparams.n_gpu_layers, hparams.n_layer);
*gpu_cache = (ne_k + ne_v) * n_gpu_layers;
*cpu_cache = (ne_k + ne_v) * (llama_model_n_layers(model) - n_gpu_layers);
*gpu_cache = (nb_k + nb_v) * n_gpu_layers;
*cpu_cache = (nb_k + nb_v) * (llama_model_n_layers(model) - n_gpu_layers);
} else {
*gpu_cache = 0;
*cpu_cache = (ne_k + ne_v) * llama_model_n_layers(model);
*cpu_cache = (nb_k + nb_v) * llama_model_n_layers(model);
}
}
void llama_kv_size(
uint64_t * cpu_cache,
uint64_t * gpu_cache,
const struct llama_model * model,
const struct llama_context_params cparams,
bool use_gpu) {
const llama_hparams hparams = model->hparams;
const int64_t n_layer = llama_model_n_layers(model);
const int64_t n_ctx = cparams.n_ctx;
const int64_t n_history = cparams.n_predict;
const int64_t n_pad = cparams.flash_attn ? 256u : 32u;
const int64_t n_kv = std::min(n_ctx, std::max(n_pad, GGML_PAD(n_history, n_pad)));
const int64_t n_embd_k_gqa = static_cast<int64_t>(hparams.n_embd_k_gqa());
const int64_t n_embd_v_gqa = static_cast<int64_t>(hparams.n_embd_v_gqa());
const int64_t nb_k = n_embd_k_gqa * n_kv * ggml_type_size(cparams.type_k);
const int64_t nb_v = n_embd_v_gqa * n_kv * ggml_type_size(cparams.type_v);
if (use_gpu) {
const int64_t n_gpu_layers = std::min(n_layer, static_cast<int64_t>(cparams.n_gpu_layers));
*gpu_cache = (nb_k + nb_v) * n_gpu_layers;
*cpu_cache = (nb_k + nb_v) * (n_layer - n_gpu_layers);
} else {
*gpu_cache = 0;
*cpu_cache = (nb_k + nb_v) * n_layer;
}
}
@ -20934,20 +20969,23 @@ void llama_model_n_flops(
struct llama_model_loader * ml,
struct model_flops * n_flops,
struct model_params * n_params,
const int64_t n_input,
const int64_t n_history,
enum ggml_type * inp_embd_dtype) {
const int64_t n_history,
const int64_t n_ctx,
enum ggml_type * inp_embd_dtype,
bool flash_attn) {
const llama_hparams hparams = model->hparams;
const int64_t n_layer = hparams.n_layer;
const int64_t n_vocab = hparams.n_vocab;
const int64_t n_embd = hparams.n_embd;
const int64_t n_head = hparams.n_head();
const int64_t n_head_kv = hparams.n_head_kv();
const int64_t n_ff = hparams.n_ff();
const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
const int64_t n_embd_head_k = hparams.n_embd_head_k;
const int64_t n_embd_head_v = hparams.n_embd_head_v;
const int64_t n_expert = hparams.n_expert;
const int64_t n_pad = flash_attn ? 256u : 32u;
const int64_t n_kv = std::min(n_ctx, std::max(n_pad, GGML_PAD(n_history, n_pad)));
// assign all the tensors on CPU by default
model->buft_input = llama_default_buffer_type_cpu(*model, true);
model->buft_output = llama_default_buffer_type_cpu(*model, true);
@ -21045,64 +21083,66 @@ void llama_model_n_flops(
break;
}
case 2: { // "output_norm.weight"
count_n_flops (n_flops, cur->type, PROFILER_LAYER_OUTPUT, n_input * (4 * n_embd + 1));
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_OUTPUT, 4 * n_embd + 1); // rms_norm
count_n_flops (n_flops, cur->type, PROFILER_LAYER_OUTPUT, n_embd); // norm weights
count_n_params(n_params, cur->type, PROFILER_LAYER_OUTPUT, ggml_nelements(cur));
break;
}
case 3: { // "output.weight"
count_n_flops (n_flops, cur->type, PROFILER_LAYER_OUTPUT, 2 * n_input * n_embd * n_vocab);
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_OUTPUT, 5 * n_input * n_vocab); // softmax
count_n_flops (n_flops, cur->type, PROFILER_LAYER_OUTPUT, 2 * n_embd * n_vocab);
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_OUTPUT, 5 * n_vocab); // softmax
count_n_params(n_params, cur->type, PROFILER_LAYER_OUTPUT, ggml_nelements(cur));
break;
}
case 4: // "blk.0.attn_norm.weight"
case 12: // "blk.0.ffn_norm.weight"
{
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, n_input * (4 * n_embd + 1));
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, 4 * n_embd + 1); // rms norm
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, n_embd); // norm weights
count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
break;
}
case 5: { // "blk.0.attn_q.weight"
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * (n_head * n_embd_head_k));
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd_head_k); // rope
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_embd * n_embd);
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, 2.5 * n_embd); // rope
count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
break;
}
case 6: { // "blk.0.attn_k.weight"
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * (n_head * n_embd_k_gqa));
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd_k_gqa); // rope
count_n_flops (n_flops, GGML_TYPE_F16, PROFILER_LAYER_BACKEND, 2 * n_input * (n_input + n_history) * n_embd_head_k * n_head); // compute kq with kvcache
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, 7 * n_input * (n_input + n_history) * n_head); // scale, mask, and softmax
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_embd * n_head_kv * n_embd_head_k);
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, 2.5 * n_embd_head_k * n_head_kv); // rope
count_n_flops (n_flops, GGML_TYPE_F16, PROFILER_LAYER_BACKEND, 2 * n_embd_head_k * n_head * n_kv * n_head_kv); // compute kq with kvcache
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, 7 * n_head * n_kv); // scale, mask, and softmax
count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
break;
}
case 7: { // "blk.0.attn_v.weight"
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * (n_head * n_embd_v_gqa));
count_n_flops (n_flops, GGML_TYPE_F16, PROFILER_LAYER_BACKEND, n_input * (n_input + n_history) * n_embd_head_k * n_head); // compute kqv with kvcache
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_embd * n_head_kv * n_embd_head_v);
count_n_flops (n_flops, GGML_TYPE_F16, PROFILER_LAYER_BACKEND, 2 * n_embd_head_v * n_head * n_kv * n_head_kv); // compute kqv with kvcache
count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
break;
}
case 8: { // "blk.0.attn_output.weight"
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * (n_head * n_embd_head_k) * n_embd);
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, n_input * n_embd); // shortcut
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_embd * n_embd);
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, n_embd); // shortcut
count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
break;
}
case 9: { // "blk.0.ffn_gate.weight"
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * n_ff);
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, 5 * n_input * n_ff); // SiLU
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_embd * n_ff);
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, 8 * n_ff); // SiLU
count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
break;
}
case 10: { // "blk.0.ffn_down.weight"
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * n_ff);
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, n_input * n_embd); // shortcut
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_embd * n_ff);
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, n_embd); // shortcut
count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
break;
}
case 11: { // "blk.0.ffn_up.weight"
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * n_ff);
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, n_input * n_ff); // silu(gate(x)) * up(x)
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_embd * n_ff);
count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, n_ff); // silu(gate(x)) * up(x)
count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
break;
}
@ -21117,20 +21157,20 @@ void llama_model_n_flops(
case 17: // "blk.0.attn_output.bias"
case 19: // "blk.0.ffn_down.bias"
{
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, n_input * n_embd);
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, n_embd);
count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
break;
}
case 18: // "blk.0.ffn_gate.bias"
case 20: // "blk.0.ffn_up.bias"
{
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, n_input * n_ff);
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, n_ff);
count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
break;
}
// optional: expert tensors
case 21: { // "blk.0.ffn_gate_inp.weight"
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * n_expert);
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_embd * n_expert);
count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
break;
}
@ -21138,7 +21178,7 @@ void llama_model_n_flops(
case 23: // "blk.0.ffn_down_exps.weight"
case 24: // "blk.0.ffn_up_exps.weight"
{
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * n_ff * n_expert);
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_embd * n_ff * n_expert);
count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
break;
}