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count model flops for f32xf32, f16xf32, q4kxf32, q6kxf32

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Lizonghang 2024-11-24 13:13:32 +04:00
parent a5ba34169a
commit 3fe00a16a0
4 changed files with 188 additions and 119 deletions
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140
src/llama.cpp
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@ -3549,8 +3549,8 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_
void llama_profile_device(device_info * dev_info, struct llama_model * model, llama_model_loader * ml, const char * test_file, int n_threads) {
dev_info->device_name = device_name();
dev_info->cpu_props.cores = device_cpu_cores();
dev_info->cpu_props.flops_f32 = device_cpu_flops(model, GGML_TYPE_F32, GGML_TYPE_F32, n_threads);
dev_info->cpu_props.flops_f16 = device_cpu_flops(model, GGML_TYPE_F16, GGML_TYPE_F16, n_threads);
dev_info->cpu_props.flops_f32_f32 = device_cpu_flops(model, GGML_TYPE_F32, GGML_TYPE_F32, n_threads);
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);
@ -3582,18 +3582,19 @@ 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_flops_f32 = device_metal_flops(model, GGML_TYPE_F32, GGML_TYPE_F32);
dev_info->gpu_props.metal_flops_f16 = device_metal_flops(model, GGML_TYPE_F16, GGML_TYPE_F16);
dev_info->gpu_props.metal_flops_f32_f32 = device_metal_flops(model, GGML_TYPE_F32, GGML_TYPE_F32);
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.cuda_flops_f32 = device_cuda_flops (model, GGML_TYPE_F32, GGML_TYPE_F32);
dev_info->gpu_props.cuda_flops_f16 = device_cuda_flops (model, GGML_TYPE_F16, GGML_TYPE_F16);
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);
if (dev_info->rank == 0) {
struct model_flops * ffo = &dev_info->model_flops;
llama_model_n_flops(model, ml, ffo, 1, 10);
struct model_flops * n_flops = &dev_info->model_flops;
struct model_params * n_params = &dev_info->model_params;
llama_model_n_flops(model, ml, n_flops, n_params, 1, 10);
}
}
@ -20669,7 +20670,46 @@ static void llama_model_reset_tensors(struct llama_model * model) {
model->cls_out_b = nullptr;
}
void llama_model_n_flops(struct llama_model * model, struct llama_model_loader * ml, struct model_flops * ffo, const int64_t n_input, const int64_t n_history) {
static void count_n_flops(struct model_flops * n_flops, enum ggml_type dtype, enum profiler_layer_type ltype, int64_t n) {
switch (ltype) {
case PROFILER_LAYER_OUTPUT:
switch (dtype) {
case GGML_TYPE_F32:
n_flops->output_f32_f32 += n;
break;
case GGML_TYPE_Q6_K:
n_flops->output_q6k_f32 += n;
break;
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_flops->layer_f32_f32 += n;
break;
case GGML_TYPE_F16:
n_flops->layer_f16_f32 += n;
break;
case GGML_TYPE_Q4_K:
n_flops->layer_q4k_f32 += n;
break;
case GGML_TYPE_Q6_K:
n_flops->layer_q6k_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");
}
}
void llama_model_n_flops(struct llama_model * model, 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) {
const llama_hparams hparams = model->hparams;
const int64_t n_layer = hparams.n_layer;
const int64_t n_vocab = hparams.n_vocab;
@ -20774,73 +20814,73 @@ 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"
ffo->input_flops += (2 * n_input * n_embd * n_vocab - n_input * n_embd);
ffo->input_params += static_cast<int64_t>(ggml_nelements(cur));
n_params->input_params += static_cast<int64_t>(ggml_nelements(cur));
break;
}
case 2: { // "output_norm.weight"
ffo->output_flops += n_input * (8 * n_embd + 1);
ffo->output_params += static_cast<int64_t>(ggml_nelements(cur));
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));
break;
}
case 3: { // "output.weight"
ffo->output_flops += 2 * n_input * n_embd * n_vocab;
ffo->output_flops += 5 * n_input * n_vocab;
ffo->output_params += static_cast<int64_t>(ggml_nelements(cur));
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));
break;
}
case 4: // "blk.0.attn_norm.weight"
case 12: // "blk.0.ffn_norm.weight"
{
ffo->layer_flops += n_input * (8 * n_embd + 1);
ffo->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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));
break;
}
case 5: { // "blk.0.attn_q.weight"
ffo->layer_flops += 2 * n_input * n_embd * (n_head * n_embd_head_k);
ffo->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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));
break;
}
case 6: { // "blk.0.attn_k.weight"
ffo->layer_flops += 2 * n_input * n_embd * (n_head * n_embd_k_gqa);
ffo->layer_flops += 2 * n_input * (n_input + n_history) * n_embd_head_k * n_head; // Q*K with KVCache
ffo->layer_flops += 7 * n_input * (n_input + n_history) * n_head; // scale, mask, and softmax
ffo->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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
n_params->layer_params += static_cast<int64_t>(ggml_nelements(cur));
break;
}
case 7: { // "blk.0.attn_v.weight"
ffo->layer_flops += 2 * n_input * n_embd * (n_head * n_embd_v_gqa);
ffo->layer_flops += n_input * (n_input + n_history) * n_embd_head_k * n_head; // QKV with KVCache
ffo->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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));
break;
}
case 8: { // "blk.0.attn_output.weight"
ffo->layer_flops += 2 * n_input * (n_head * n_embd_head_k) * n_embd;
ffo->layer_flops += n_input * n_embd; // shortcut
ffo->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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));
break;
}
case 9: { // "blk.0.ffn_gate.weight"
ffo->layer_flops += 2 * n_input * n_embd * n_ff;
ffo->layer_flops += 5 * n_input * n_ff; // SiLU
ffo->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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));
break;
}
case 10: { // "blk.0.ffn_down.weight"
ffo->layer_flops += 2 * n_input * n_embd * n_ff;
ffo->layer_flops += n_input * n_embd; // shortcut
ffo->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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));
break;
}
case 11: { // "blk.0.ffn_up.weight"
ffo->layer_flops += 2 * n_input * n_embd * n_ff;
ffo->layer_flops += n_input * n_ff; // silu(gate(x)) * up(x)
ffo->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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));
break;
}
case 13: { // rope_freqs.weight, for Q and K
ffo->layer_flops += 8 * n_input * n_head * n_embd_head_k;
ffo->layer_params += static_cast<int64_t>(ggml_nelements(cur));
case 13: { // rope_freqs.weight, has been counted in q and k
n_params->layer_params += static_cast<int64_t>(ggml_nelements(cur));
break;
}
// optional: bias tensors
@ -20850,29 +20890,29 @@ void llama_model_n_flops(struct llama_model * model, struct llama_model_loader *
case 17: // "blk.0.attn_output.bias"
case 19: // "blk.0.ffn_down.bias"
{
ffo->layer_flops += n_input * n_embd;
ffo->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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));
break;
}
case 18: // "blk.0.ffn_gate.bias"
case 20: // "blk.0.ffn_up.bias"
{
ffo->layer_flops += n_input * n_ff;
ffo->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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));
break;
}
// optional: expert tensors
case 21: { // "blk.0.ffn_gate_inp.weight"
ffo->layer_flops += 2 * n_input * n_embd * n_expert;
ffo->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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));
break;
}
case 22: // "blk.0.ffn_gate_exps.weight"
case 23: // "blk.0.ffn_down_exps.weight"
case 24: // "blk.0.ffn_up_exps.weight"
{
ffo->layer_flops += 2 * n_input * n_embd * n_ff * n_expert;
ffo->layer_params += static_cast<int64_t>(ggml_nelements(cur));
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));
break;
}
default: