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https://github.com/Lizonghang/prima.cpp.git
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llama_model_n_flops: remove ctxs
This commit is contained in:
Lizonghang
parent
f1c1d1b929
commit
a46d56cc60
1 changed files with 122 additions and 127 deletions
249
src/llama.cpp
249
src/llama.cpp
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@ -3571,23 +3571,23 @@ static bool is_dtype_exist(struct model_params * n_params, enum ggml_type dtype)
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}
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void llama_profile_device(device_info * dev_info, struct llama_model * model, llama_model_loader * ml, int n_threads) {
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struct model_flops * n_flops = &dev_info->model_flops;
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struct model_params * n_params = &dev_info->model_params;
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if (dev_info->rank == 0) {
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enum ggml_type inp_embd_dtype = GGML_TYPE_F32;
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llama_model_n_flops(model, ml, n_flops, n_params, 1, 32, &inp_embd_dtype);
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n_flops->inp_embd_ms = device_inp_embd_delay(model, inp_embd_dtype, 1, n_threads);
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}
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dev_info->device_name = device_name();
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dev_info->cpu_props.cores = device_cpu_cores();
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dev_info->memory.total_physical = round(device_physical_memory(false) / (double)(1 << 30) * 100) / 100;
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dev_info->memory.available_physical = round(device_physical_memory(true) / (double)(1 << 30) * 100) / 100;
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dev_info->memory.total_swap = round(device_swap_memory(false) / (double)(1 << 30) * 100) / 100;
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dev_info->memory.available_swap = round(device_swap_memory(true) / (double)(1 << 30) * 100) / 100;
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dev_info->memory.cpu_read_ram_bw = device_memory_bw(n_threads);
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struct model_flops * n_flops = &dev_info->model_flops;
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struct model_params * n_params = &dev_info->model_params;
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if (dev_info->rank == 0) {
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enum ggml_type inp_embd_dtype = GGML_TYPE_F32;
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llama_model_n_flops(model, ml, n_flops, n_params, 1, 32, &inp_embd_dtype);
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n_flops->inp_embd_ms = device_inp_embd_delay(model, inp_embd_dtype, 1, n_threads);
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}
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device_disk_seq_bw(&dev_info->disk.read_seq_bw, &dev_info->disk.write_seq_bw, n_threads);
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device_disk_rnd_bw(&dev_info->disk.read_rnd_bw, &dev_info->disk.write_rnd_bw, n_threads);
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@ -20966,6 +20966,7 @@ void llama_model_n_flops(
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buft_layer_count[model->buft_layer[i].buft]++;
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buft_layer_count[model->buft_layer[i].buft_matrix]++;
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}
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GGML_ASSERT(buft_layer_count.size() == 1);
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// create one context per buffer type
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size_t ctx_size = ggml_tensor_overhead() * (ml->n_tensors + 1);
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@ -20974,19 +20975,18 @@ void llama_model_n_flops(
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ctx_size += ggml_tensor_overhead() * n_layer * 3;
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std::map<ggml_backend_buffer_type_t, ggml_context *> ctx_map;
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std::vector<struct ggml_context *> ctxs;
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struct ggml_context * ctx = nullptr;
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for (auto & it : buft_layer_count) {
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struct ggml_init_params params = {
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/*.mem_size =*/ ctx_size,
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ true,
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};
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ggml_context * ctx = ggml_init(params);
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ctx = ggml_init(params);
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if (!ctx) {
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throw std::runtime_error(format("failed to create context\n"));
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}
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ctx_map[it.first] = ctx;
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ctxs.push_back(ctx);
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}
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const uint32_t n_layer_window[32] = {(uint32_t)n_layer};
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@ -21035,118 +21035,116 @@ void llama_model_n_flops(
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{"blk.0.ffn_up_exps.weight", 24},
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};
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for (ggml_context * ctx : ctxs) {
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for (auto * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
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auto it = tensor_name_map.find(ggml_get_name(cur));
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if (it != tensor_name_map.end()) {
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switch (it->second) {
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case 1: { // "token_embd.weight"
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count_n_params(n_params, cur->type, PROFILER_LAYER_INPUT, ggml_nelements(cur));
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*inp_embd_dtype = cur->type;
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break;
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}
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case 2: { // "output_norm.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_OUTPUT, n_input * (4 * n_embd + 1));
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count_n_params(n_params, cur->type, PROFILER_LAYER_OUTPUT, ggml_nelements(cur));
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break;
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}
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case 3: { // "output.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_OUTPUT, 2 * n_input * n_embd * n_vocab);
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count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_OUTPUT, 5 * n_input * n_vocab); // softmax
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count_n_params(n_params, cur->type, PROFILER_LAYER_OUTPUT, ggml_nelements(cur));
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break;
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}
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case 4: // "blk.0.attn_norm.weight"
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case 12: // "blk.0.ffn_norm.weight"
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{
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, n_input * (4 * n_embd + 1));
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 5: { // "blk.0.attn_q.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * (n_head * n_embd_head_k));
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count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd_head_k); // rope
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 6: { // "blk.0.attn_k.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * (n_head * n_embd_k_gqa));
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count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd_k_gqa); // rope
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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
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count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, 7 * n_input * (n_input + n_history) * n_head); // scale, mask, and softmax
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 7: { // "blk.0.attn_v.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * (n_head * n_embd_v_gqa));
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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
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 8: { // "blk.0.attn_output.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * (n_head * n_embd_head_k) * n_embd);
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count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, n_input * n_embd); // shortcut
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 9: { // "blk.0.ffn_gate.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * n_ff);
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count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, 5 * n_input * n_ff); // SiLU
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 10: { // "blk.0.ffn_down.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * n_ff);
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count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, n_input * n_embd); // shortcut
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 11: { // "blk.0.ffn_up.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * n_ff);
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count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, n_input * n_ff); // silu(gate(x)) * up(x)
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 13: { // rope_freqs.weight, has been counted in q and k
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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// optional: bias tensors
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case 14: // "blk.0.attn_q.bias"
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case 15: // "blk.0.attn_k.bias"
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case 16: // "blk.0.attn_v.bias"
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case 17: // "blk.0.attn_output.bias"
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case 19: // "blk.0.ffn_down.bias"
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{
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, n_input * n_embd);
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 18: // "blk.0.ffn_gate.bias"
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case 20: // "blk.0.ffn_up.bias"
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{
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, n_input * n_ff);
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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// optional: expert tensors
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case 21: { // "blk.0.ffn_gate_inp.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * n_expert);
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 22: // "blk.0.ffn_gate_exps.weight"
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case 23: // "blk.0.ffn_down_exps.weight"
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case 24: // "blk.0.ffn_up_exps.weight"
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{
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * n_ff * n_expert);
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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default:
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LLAMA_LOG_INFO("Uncaught tensor\n");
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return;
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for (auto * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
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auto it = tensor_name_map.find(ggml_get_name(cur));
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if (it != tensor_name_map.end()) {
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switch (it->second) {
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case 1: { // "token_embd.weight"
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count_n_params(n_params, cur->type, PROFILER_LAYER_INPUT, ggml_nelements(cur));
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*inp_embd_dtype = cur->type;
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break;
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}
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case 2: { // "output_norm.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_OUTPUT, n_input * (4 * n_embd + 1));
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count_n_params(n_params, cur->type, PROFILER_LAYER_OUTPUT, ggml_nelements(cur));
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break;
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}
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case 3: { // "output.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_OUTPUT, 2 * n_input * n_embd * n_vocab);
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count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_OUTPUT, 5 * n_input * n_vocab); // softmax
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count_n_params(n_params, cur->type, PROFILER_LAYER_OUTPUT, ggml_nelements(cur));
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break;
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}
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case 4: // "blk.0.attn_norm.weight"
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case 12: // "blk.0.ffn_norm.weight"
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{
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, n_input * (4 * n_embd + 1));
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 5: { // "blk.0.attn_q.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * (n_head * n_embd_head_k));
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count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd_head_k); // rope
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 6: { // "blk.0.attn_k.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * (n_head * n_embd_k_gqa));
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count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd_k_gqa); // rope
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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
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count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, 7 * n_input * (n_input + n_history) * n_head); // scale, mask, and softmax
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 7: { // "blk.0.attn_v.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * (n_head * n_embd_v_gqa));
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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
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 8: { // "blk.0.attn_output.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * (n_head * n_embd_head_k) * n_embd);
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count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, n_input * n_embd); // shortcut
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 9: { // "blk.0.ffn_gate.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * n_ff);
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count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, 5 * n_input * n_ff); // SiLU
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 10: { // "blk.0.ffn_down.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * n_ff);
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count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, n_input * n_embd); // shortcut
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 11: { // "blk.0.ffn_up.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * n_ff);
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count_n_flops (n_flops, GGML_TYPE_F32, PROFILER_LAYER_BACKEND, n_input * n_ff); // silu(gate(x)) * up(x)
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 13: { // rope_freqs.weight, has been counted in q and k
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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// optional: bias tensors
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case 14: // "blk.0.attn_q.bias"
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case 15: // "blk.0.attn_k.bias"
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case 16: // "blk.0.attn_v.bias"
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case 17: // "blk.0.attn_output.bias"
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case 19: // "blk.0.ffn_down.bias"
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{
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, n_input * n_embd);
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 18: // "blk.0.ffn_gate.bias"
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case 20: // "blk.0.ffn_up.bias"
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{
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, n_input * n_ff);
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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// optional: expert tensors
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case 21: { // "blk.0.ffn_gate_inp.weight"
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * n_expert);
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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case 22: // "blk.0.ffn_gate_exps.weight"
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case 23: // "blk.0.ffn_down_exps.weight"
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case 24: // "blk.0.ffn_up_exps.weight"
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{
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count_n_flops (n_flops, cur->type, PROFILER_LAYER_BACKEND, 2 * n_input * n_embd * n_ff * n_expert);
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count_n_params(n_params, cur->type, PROFILER_LAYER_BACKEND, ggml_nelements(cur));
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break;
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}
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default:
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LLAMA_LOG_INFO("Uncaught tensor\n");
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return;
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}
|
||||
}
|
||||
}
|
||||
|
|
@ -21155,10 +21153,7 @@ void llama_model_n_flops(
|
|||
ml->n_created = 0;
|
||||
ml->size_data = 0;
|
||||
llama_model_reset_tensors(model);
|
||||
for (ggml_context * ctx : ctxs) {
|
||||
ggml_free(ctx);
|
||||
}
|
||||
ctxs.clear();
|
||||
ggml_free(ctx);
|
||||
ctx_map.clear();
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue