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add q80xf32 and count_n_params

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Lizonghang 2024-11-24 23:11:12 +04:00
parent 3fe00a16a0
commit a7a95b53fe
3 changed files with 297 additions and 69 deletions
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139
common/profiler.cpp
View file

@ -31,6 +31,7 @@
#include <sstream>
#include <sys/types.h>
#include <vector>
#include <inttypes.h>
const char * device_name() {
static char device_name[256];
@ -489,6 +490,12 @@ void device_print_props(struct device_info * dev_info_set, int n, struct llama_m
}
LOG_INF("\n");
LOG_INF("| CPU flops (Q80 x F32, GFLOPS)");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].cpu_props.flops_q80_f32);
}
LOG_INF("\n");
LOG_INF("| Physical Mem Total (GB) ");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.2f ", dev_info_set[i].memory.total_physical);
@ -615,6 +622,12 @@ void device_print_props(struct device_info * dev_info_set, int n, struct llama_m
}
LOG_INF("\n");
LOG_INF("| Metal flops (Q80xF32, GFLOPS)");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].gpu_props.metal_flops_q80_f32);
}
LOG_INF("\n");
LOG_INF("| CUDA flops (F32xF32, GFLOPS)");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].gpu_props.cuda_flops_f32_f32);
@ -639,49 +652,119 @@ void device_print_props(struct device_info * dev_info_set, int n, struct llama_m
}
LOG_INF("\n");
LOG_INF("| CUDA flops (Q80xF32, GFLOPS)");
for (int i = 0; i < n; ++i) {
LOG_INF("| %-10.1f ", dev_info_set[i].gpu_props.cuda_flops_q80_f32);
}
LOG_INF("\n");
LOG_INF("| Model flops (output F32xF32) ");
LOG_INF("| %-10lu ", dev_info_set[0].model_flops.output_f32_f32);
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.output_f32_f32);
LOG_INF("\n");
LOG_INF("| Model flops (output F16xF32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.output_f16_f32);
LOG_INF("\n");
LOG_INF("| Model flops (output Q4KxF32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.output_q4k_f32);
LOG_INF("\n");
LOG_INF("| Model flops (output Q6KxF32) ");
LOG_INF("| %-10lu ", dev_info_set[0].model_flops.output_q6k_f32);
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.output_q6k_f32);
LOG_INF("\n");
LOG_INF("| Model flops (output Q80xF32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.output_q80_f32);
LOG_INF("\n");
LOG_INF("| Model flops (layer F32xF32) ");
LOG_INF("| %-10lu ", dev_info_set[0].model_flops.layer_f32_f32);
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.layer_f32_f32);
LOG_INF("\n");
LOG_INF("| Model flops (layer F16xF32) ");
LOG_INF("| %-10lu ", dev_info_set[0].model_flops.layer_f16_f32);
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.layer_f16_f32);
LOG_INF("\n");
LOG_INF("| Model flops (layer Q4KxF32) ");
LOG_INF("| %-10lu ", dev_info_set[0].model_flops.layer_q4k_f32);
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.layer_q4k_f32);
LOG_INF("\n");
LOG_INF("| Model flops (layer Q6KxF32) ");
LOG_INF("| %-10lu ", dev_info_set[0].model_flops.layer_q6k_f32);
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.layer_q6k_f32);
LOG_INF("\n");
LOG_INF("| Model params (input) ");
LOG_INF("| %-10lu ", dev_info_set[0].model_params.input_params);
LOG_INF("| Model flops (layer Q80xF32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_flops.layer_q80_f32);
LOG_INF("\n");
LOG_INF("| Model params (each layer) ");
LOG_INF("| %-10lu ", dev_info_set[0].model_params.layer_params);
LOG_INF("| Model params (input F32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.input_f32);
LOG_INF("\n");
LOG_INF("| Model params (output) ");
LOG_INF("| %-10lu ", dev_info_set[0].model_params.output_params);
LOG_INF("| Model params (input F16) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.input_f16);
LOG_INF("\n");
LOG_INF("| Model params (input Q4K) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.input_q4k);
LOG_INF("\n");
LOG_INF("| Model params (input Q6K) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.input_q6k);
LOG_INF("\n");
LOG_INF("| Model params (input Q80) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.input_q80);
LOG_INF("\n");
LOG_INF("| Model params (layer F32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.layer_f32);
LOG_INF("\n");
LOG_INF("| Model params (layer F16) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.layer_f16);
LOG_INF("\n");
LOG_INF("| Model params (layer Q4K) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.layer_q4k);
LOG_INF("\n");
LOG_INF("| Model params (layer Q6K) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.layer_q6k);
LOG_INF("\n");
LOG_INF("| Model params (layer Q80) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.layer_q80);
LOG_INF("\n");
LOG_INF("| Model params (output F32) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.output_f32);
LOG_INF("\n");
LOG_INF("| Model params (output F16) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.output_f16);
LOG_INF("\n");
LOG_INF("| Model params (output Q4K) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.output_q4k);
LOG_INF("\n");
LOG_INF("| Model params (output Q6K) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.output_q6k);
LOG_INF("\n");
LOG_INF("| Model params (output Q80) ");
LOG_INF("| %-10" PRId64 " ", dev_info_set[0].model_params.output_q80);
LOG_INF("\n");
model_flops ffo = dev_info_set[0].model_flops;
int64_t total_flops = ffo.output_f32_f32 + (ffo.layer_f32_f32 * llama_model_n_layers(model)); // todo
double cpu_flops_f16 = dev_info_set[0].cpu_props.flops_f16_f32 * 1e9;
LOG_INF("| Token latency (ms) ");
LOG_INF("| %-10.2f ", total_flops / cpu_flops_f16 * 1000);
LOG_INF("\n");
// LOG_INF("| Token latency (ms) ");
// LOG_INF("| %-10.2f ", total_flops / cpu_flops_f16 * 1000);
// LOG_INF("\n");
LOG_INF("-------------------------------------------------------------------------------------------\n\n");
}
@ -704,12 +787,12 @@ size_t serialize(const struct device_info * dev_info, char ** buffer) {
+ gpu_description_len
+ sizeof(float) // disk_read_bandwidth
+ sizeof(uint32_t) // cpu_props.cores
+ sizeof(float) * 4 // cpu_props.flops_f32, cpu_props.flops_f16, cpu_props.flops_q4k_f32, cpu_props.flops_q6k_f32
+ sizeof(float) * 5 // cpu_props.flops_f32_f32, cpu_props.flops_f16_f32, cpu_props.flops_q4k_f32, cpu_props.flops_q6k_f32, cpu_props.flops_q80_f32
+ sizeof(struct memory_info)
+ sizeof(struct gpu_support)
+ sizeof(float) * 10; // gpu_props.memory_free, gpu_props.memory_total,
// gpu_props.metal_flops_f32, gpu_props.metal_flops_f16, gpu_props.metal_flops_q4k_f32, gpu_props.metal_flops_q6k_f32,
// gpu_props.cuda_flops_f32, gpu_props.cuda_flops_f16, gpu_props.cuda_flops_q8, and gpu_props.cuda_flops_q4k
+ sizeof(float) * 12; // gpu_props.memory_free, gpu_props.memory_total,
// gpu_props.metal_flops_f32_f32, gpu_props.metal_flops_f16_f32, gpu_props.metal_flops_q4k_f32, gpu_props.metal_flops_q6k_f32, gpu_props.metal_flops_q80_f32,
// gpu_props.cuda_flops_f32_f32, gpu_props.cuda_flops_f16_f32, gpu_props.cuda_flops_q4k_f32, gpu_props.cuda_flops_q6k_f32, gpu_props.cuda_flops_q80_f32
*buffer = (char *)malloc(total_size);
char * ptr = *buffer;
@ -763,6 +846,9 @@ size_t serialize(const struct device_info * dev_info, char ** buffer) {
memcpy(ptr, &dev_info->cpu_props.flops_q6k_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->cpu_props.flops_q80_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->memory, sizeof(struct memory_info));
ptr += sizeof(struct memory_info);
@ -787,6 +873,9 @@ size_t serialize(const struct device_info * dev_info, char ** buffer) {
memcpy(ptr, &dev_info->gpu_props.metal_flops_q6k_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->gpu_props.metal_flops_q80_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->gpu_props.cuda_flops_f32_f32, sizeof(float));
ptr += sizeof(float);
@ -797,6 +886,9 @@ size_t serialize(const struct device_info * dev_info, char ** buffer) {
ptr += sizeof(float);
memcpy(ptr, &dev_info->gpu_props.cuda_flops_q6k_f32, sizeof(float));
ptr += sizeof(float);
memcpy(ptr, &dev_info->gpu_props.cuda_flops_q80_f32, sizeof(float));
// no need to synchronize model flops and model params
return total_size;
@ -868,6 +960,9 @@ void deserialize(const char * buffer, struct device_info * dev_info) {
memcpy(&dev_info->cpu_props.flops_q6k_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->cpu_props.flops_q80_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->memory, ptr, sizeof(struct memory_info));
ptr += sizeof(struct memory_info);
@ -892,6 +987,9 @@ void deserialize(const char * buffer, struct device_info * dev_info) {
memcpy(&dev_info->gpu_props.metal_flops_q6k_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->gpu_props.metal_flops_q80_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->gpu_props.cuda_flops_f32_f32, ptr, sizeof(float));
ptr += sizeof(float);
@ -902,6 +1000,9 @@ void deserialize(const char * buffer, struct device_info * dev_info) {
ptr += sizeof(float);
memcpy(&dev_info->gpu_props.cuda_flops_q6k_f32, ptr, sizeof(float));
ptr += sizeof(float);
memcpy(&dev_info->gpu_props.cuda_flops_q80_f32, ptr, sizeof(float));
// no need to synchronize model flops and model params
}

56
common/profiler.h
View file

@ -12,6 +12,7 @@ struct cpu_props {
float flops_f16_f32; // in GFLOPS
float flops_q4k_f32; // in GFLOPS
float flops_q6k_f32; // in GFLOPS
float flops_q80_f32; // in GFLOPS
cpu_props() :
name(""),
@ -20,7 +21,8 @@ struct cpu_props {
flops_f32_f32(0.0f),
flops_f16_f32(0.0f),
flops_q4k_f32(0.0f),
flops_q6k_f32(0.0f) {}
flops_q6k_f32(0.0f),
flops_q80_f32(0.0f) {}
};
struct memory_info {
@ -66,10 +68,12 @@ struct gpu_props {
float metal_flops_f16_f32; // in GFLOPS
float metal_flops_q4k_f32; // in GFLOPS
float metal_flops_q6k_f32; // in GFLOPS
float metal_flops_q80_f32; // in GFLOPS
float cuda_flops_f32_f32; // in GFLOPS
float cuda_flops_f16_f32; // in GFLOPS
float cuda_flops_q4k_f32; // in GFLOPS
float cuda_flops_q6k_f32; // in GFLOPS
float cuda_flops_q80_f32; // in GFLOPS
gpu_props() :
name(""),
@ -80,38 +84,72 @@ struct gpu_props {
metal_flops_f16_f32(0.0f),
metal_flops_q4k_f32(0.0f),
metal_flops_q6k_f32(0.0f),
metal_flops_q80_f32(0.0f),
cuda_flops_f32_f32 (0.0f),
cuda_flops_f16_f32 (0.0f),
cuda_flops_q4k_f32 (0.0f),
cuda_flops_q6k_f32 (0.0f) {}
cuda_flops_q6k_f32 (0.0f),
cuda_flops_q80_f32 (0.0f) {}
};
struct model_flops {
int64_t output_f32_f32;
int64_t output_f16_f32;
int64_t output_q4k_f32;
int64_t output_q6k_f32;
int64_t output_q80_f32;
int64_t layer_f32_f32;
int64_t layer_f16_f32;
int64_t layer_q4k_f32;
int64_t layer_q6k_f32;
int64_t layer_q80_f32;
model_flops() :
output_f32_f32(0),
output_f16_f32(0),
output_q4k_f32(0),
output_q6k_f32(0),
output_q80_f32(0),
layer_f32_f32 (0),
layer_f16_f32 (0),
layer_q4k_f32 (0),
layer_q6k_f32 (0) {}
layer_q6k_f32 (0),
layer_q80_f32 (0) {}
};
struct model_params {
int64_t input_params;
int64_t output_params;
int64_t layer_params;
int64_t input_f32;
int64_t input_f16;
int64_t input_q4k;
int64_t input_q6k;
int64_t input_q80;
int64_t output_f32;
int64_t output_f16;
int64_t output_q4k;
int64_t output_q6k;
int64_t output_q80;
int64_t layer_f32;
int64_t layer_f16;
int64_t layer_q4k;
int64_t layer_q6k;
int64_t layer_q80;
model_params() :
input_params (0),
output_params(0),
layer_params (0) {}
input_f32 (0),
input_f16 (0),
input_q4k (0),
input_q6k (0),
input_q80 (0),
output_f32(0),
output_f16(0),
output_q4k(0),
output_q6k(0),
output_q80(0),
layer_f32 (0),
layer_f16 (0),
layer_q4k (0),
layer_q6k (0),
layer_q80 (0) {}
};
struct device_info {

123
src/llama.cpp
View file

@ -91,6 +91,7 @@
#include <unordered_map>
#include <chrono>
#include <regex>
#include <inttypes.h>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
@ -110,7 +111,7 @@ struct Timer {
~Timer() {
if (enable_timer) {
int64_t end_time = ggml_time_us();
LLAMA_LOG_INFO("Time to run %s: %lu ms\n", name, (end_time - start_time)/1000);
LLAMA_LOG_INFO("Time to run %s: %" PRId64 " ms\n", name, (end_time - start_time) / 1000);
}
}
};
@ -3553,6 +3554,7 @@ void llama_profile_device(device_info * dev_info, struct llama_model * model, ll
dev_info->cpu_props.flops_f16_f32 = device_cpu_flops(model, GGML_TYPE_F16, GGML_TYPE_F32, n_threads);
dev_info->cpu_props.flops_q4k_f32 = device_cpu_flops(model, GGML_TYPE_Q4_K, GGML_TYPE_F32, n_threads);
dev_info->cpu_props.flops_q6k_f32 = device_cpu_flops(model, GGML_TYPE_Q6_K, GGML_TYPE_F32, n_threads);
dev_info->cpu_props.flops_q80_f32 = device_cpu_flops(model, GGML_TYPE_Q8_0, GGML_TYPE_F32, n_threads);
dev_info->memory.total_physical = round(device_physical_memory(false) / (double)(1 << 30) * 100) / 100;
dev_info->memory.available_physical = round(device_physical_memory(true) / (double)(1 << 30) * 100) / 100;
@ -3586,10 +3588,12 @@ void llama_profile_device(device_info * dev_info, struct llama_model * model, ll
dev_info->gpu_props.metal_flops_f16_f32 = device_metal_flops(model, GGML_TYPE_F16, GGML_TYPE_F32);
dev_info->gpu_props.metal_flops_q4k_f32 = device_metal_flops(model, GGML_TYPE_Q4_K, GGML_TYPE_F32);
dev_info->gpu_props.metal_flops_q6k_f32 = device_metal_flops(model, GGML_TYPE_Q6_K, GGML_TYPE_F32);
dev_info->gpu_props.metal_flops_q80_f32 = device_metal_flops(model, GGML_TYPE_Q8_0, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_f32_f32 = device_cuda_flops (model, GGML_TYPE_F32, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_f16_f32 = device_cuda_flops (model, GGML_TYPE_F16, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_q4k_f32 = device_cuda_flops (model, GGML_TYPE_Q4_K, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_q6k_f32 = device_cuda_flops (model, GGML_TYPE_Q6_K, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_q80_f32 = device_cuda_flops (model, GGML_TYPE_Q8_0, GGML_TYPE_F32);
if (dev_info->rank == 0) {
struct model_flops * n_flops = &dev_info->model_flops;
@ -20677,9 +20681,18 @@ static void count_n_flops(struct model_flops * n_flops, enum ggml_type dtype, en
case GGML_TYPE_F32:
n_flops->output_f32_f32 += n;
break;
case GGML_TYPE_F16:
n_flops->output_f16_f32 += n;
break;
case GGML_TYPE_Q4_K:
n_flops->output_q4k_f32 += n;
break;
case GGML_TYPE_Q6_K:
n_flops->output_q6k_f32 += n;
break;
case GGML_TYPE_Q8_0:
n_flops->output_q80_f32 += n;
break;
default:
throw std::runtime_error("Unrecognized weight type in PROFILER_LAYER_OUTPUT\n");
}
@ -20699,6 +20712,82 @@ static void count_n_flops(struct model_flops * n_flops, enum ggml_type dtype, en
case GGML_TYPE_Q6_K:
n_flops->layer_q6k_f32 += n;
break;
case GGML_TYPE_Q8_0:
n_flops->layer_q80_f32 += n;
break;
default:
throw std::runtime_error("Unrecognized weight type in PROFILER_LAYER_BACKEND\n");
}
break;
default:
throw std::runtime_error("Unrecognized profiler layer type\n");
}
}
static void count_n_params(struct model_params * n_params, enum ggml_type dtype, enum profiler_layer_type ltype, size_t n) {
int64_t n_i64t = static_cast<int64_t>(n);
switch (ltype) {
case PROFILER_LAYER_INPUT:
switch (dtype) {
case GGML_TYPE_F32:
n_params->input_f32 += n_i64t;
break;
case GGML_TYPE_F16:
n_params->input_f16 += n_i64t;
break;
case GGML_TYPE_Q4_K:
n_params->input_q4k += n_i64t;
break;
case GGML_TYPE_Q6_K:
n_params->input_q6k += n_i64t;
break;
case GGML_TYPE_Q8_0:
n_params->input_q80 += n_i64t;
break;
default:
throw std::runtime_error("Unrecognized weight type in PROFILER_LAYER_OUTPUT\n");
}
break;
case PROFILER_LAYER_OUTPUT:
switch (dtype) {
case GGML_TYPE_F32:
n_params->output_f32 += n_i64t;
break;
case GGML_TYPE_F16:
n_params->output_f16 += n_i64t;
break;
case GGML_TYPE_Q4_K:
n_params->output_q4k += n_i64t;
break;
case GGML_TYPE_Q6_K:
n_params->output_q6k += n_i64t;
break;
case GGML_TYPE_Q8_0:
n_params->output_q80 += n_i64t;
default:
throw std::runtime_error("Unrecognized weight type in PROFILER_LAYER_OUTPUT\n");
}
break;
case PROFILER_LAYER_BACKEND:
switch (dtype) {
case GGML_TYPE_F32:
n_params->layer_f32 += n_i64t;
break;
case GGML_TYPE_F16:
n_params->layer_f16 += n_i64t;
break;
case GGML_TYPE_Q4_K:
n_params->layer_q4k += n_i64t;
break;
case GGML_TYPE_Q6_K:
n_params->layer_q6k += n_i64t;
break;
case GGML_TYPE_Q8_0:
n_params->layer_q80 += n_i64t;
break;
default:
throw std::runtime_error("Unrecognized weight type in PROFILER_LAYER_BACKEND\n");
}
@ -20814,31 +20903,31 @@ void llama_model_n_flops(struct llama_model * model, struct llama_model_loader *
if (it != tensor_name_map.end()) {
switch (it->second) {
case 1: { // "token_embd.weight"
n_params->input_params += static_cast<int64_t>(ggml_nelements(cur));
count_n_params(n_params, cur->type, PROFILER_LAYER_INPUT, ggml_nelements(cur));
break;
}
case 2: { // "output_norm.weight"
count_n_flops (n_flops, cur->type, PROFILER_LAYER_OUTPUT, n_input * (4 * n_embd + 1));
n_params->output_params += static_cast<int64_t>(ggml_nelements(cur));
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
n_params->output_params += static_cast<int64_t>(ggml_nelements(cur));
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));
n_params->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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
n_params->layer_params += static_cast<int64_t>(ggml_nelements(cur));
count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
break;
}
case 6: { // "blk.0.attn_k.weight"
@ -20846,41 +20935,41 @@ void llama_model_n_flops(struct llama_model * model, struct llama_model_loader *
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
n_params->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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
n_params->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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
n_params->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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
n_params->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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
n_params->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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)
n_params->layer_params += static_cast<int64_t>(ggml_nelements(cur));
count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
break;
}
case 13: { // rope_freqs.weight, has been counted in q and k
n_params->layer_params += static_cast<int64_t>(ggml_nelements(cur));
count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
break;
}
// optional: bias tensors
@ -20891,20 +20980,20 @@ void llama_model_n_flops(struct llama_model * model, struct llama_model_loader *
case 19: // "blk.0.ffn_down.bias"
{
count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, n_input * n_embd);
n_params->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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);
n_params->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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);
n_params->layer_params += static_cast<int64_t>(ggml_nelements(cur));
count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
break;
}
case 22: // "blk.0.ffn_gate_exps.weight"
@ -20912,7 +21001,7 @@ void llama_model_n_flops(struct llama_model * model, struct llama_model_loader *
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);
n_params->layer_params += static_cast<int64_t>(ggml_nelements(cur));
count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
break;
}
default: