// preferrably to run on the same backend as the buffer
ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
- sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false);
+ sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false, true);
// initialize buffers from a max size graph (optional)
reserve_graph = build_graph(sched, max_batch_size);
typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
// Initialize a backend scheduler, backends with low index are given priority over backends with high index
- GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel);
+ GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel, bool op_offload);
GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched);
// Initialize backend buffers from a measure graph
std::vector<std::vector<llama_model_tensor_buft_override>> tensor_buft_overrides;
std::vector<bool> use_mmap;
std::vector<bool> embeddings;
+ std::vector<bool> no_op_offload;
ggml_numa_strategy numa;
int reps;
ggml_sched_priority prio;
/* tensor_buft_overrides*/ { std::vector<llama_model_tensor_buft_override>{{nullptr,nullptr}} },
/* use_mmap */ { true },
/* embeddings */ { false },
+ /* no_op_offload */ { false },
/* numa */ GGML_NUMA_STRATEGY_DISABLED,
/* reps */ 5,
/* prio */ GGML_SCHED_PRIO_NORMAL,
join(cmd_params_defaults.embeddings, ",").c_str());
printf(" -ts, --tensor-split <ts0/ts1/..> (default: 0)\n");
printf(" -ot --override-tensors <tensor name pattern>=<buffer type>;... (default: disabled)\n");
+ printf(" -nopo, --no-op-offload <i> (default: 0)\n");
printf(" -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps);
printf(" --prio <0|1|2|3> (default: %d)\n", cmd_params_defaults.prio);
printf(" --delay <0...N> (seconds) (default: %d)\n", cmd_params_defaults.delay);
}
auto p = string_split<bool>(argv[i], split_delim);
params.embeddings.insert(params.embeddings.end(), p.begin(), p.end());
+ } else if (arg == "-nopo" || arg == "--no-op-offload") {
+ if (++i >= argc) {
+ invalid_param = true;
+ break;
+ }
+ auto p = string_split<bool>(argv[i], split_delim);
+ params.no_op_offload.insert(params.no_op_offload.end(), p.begin(), p.end());
} else if (arg == "-ts" || arg == "--tensor-split") {
if (++i >= argc) {
invalid_param = true;
if (params.embeddings.empty()) {
params.embeddings = cmd_params_defaults.embeddings;
}
+ if (params.no_op_offload.empty()) {
+ params.no_op_offload = cmd_params_defaults.no_op_offload;
+ }
if (params.n_threads.empty()) {
params.n_threads = cmd_params_defaults.n_threads;
}
std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
bool use_mmap;
bool embeddings;
+ bool no_op_offload;
llama_model_params to_llama_mparams() const {
llama_model_params mparams = llama_model_default_params();
cparams.offload_kqv = !no_kv_offload;
cparams.flash_attn = flash_attn;
cparams.embeddings = embeddings;
+ cparams.op_offload = !no_op_offload;
return cparams;
}
for (const auto & ot : params.tensor_buft_overrides)
for (const auto & mmp : params.use_mmap)
for (const auto & embd : params.embeddings)
+ for (const auto & nopo : params.no_op_offload)
for (const auto & nb : params.n_batch)
for (const auto & nub : params.n_ubatch)
for (const auto & tk : params.type_k)
/* .tensor_buft_overrides = */ ot,
/* .use_mmap = */ mmp,
/* .embeddings = */ embd,
+ /* .no_op_offload= */ nopo,
};
instances.push_back(instance);
}
/* .tensor_buft_overrides = */ ot,
/* .use_mmap = */ mmp,
/* .embeddings = */ embd,
+ /* .no_op_offload= */ nopo,
};
instances.push_back(instance);
}
/* .tensor_buft_overrides = */ ot,
/* .use_mmap = */ mmp,
/* .embeddings = */ embd,
+ /* .no_op_offload= */ nopo,
};
instances.push_back(instance);
}
std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
bool use_mmap;
bool embeddings;
+ bool no_op_offload;
int n_prompt;
int n_gen;
int n_depth;
tensor_buft_overrides = inst.tensor_buft_overrides;
use_mmap = inst.use_mmap;
embeddings = inst.embeddings;
+ no_op_offload = inst.no_op_offload;
n_prompt = inst.n_prompt;
n_gen = inst.n_gen;
n_depth = inst.n_depth;
"model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "n_threads",
"cpu_mask", "cpu_strict", "poll", "type_k", "type_v", "n_gpu_layers",
"split_mode", "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "tensor_buft_overrides",
- "use_mmap", "embeddings", "n_prompt", "n_gen", "n_depth", "test_time",
+ "use_mmap", "embeddings", "no_op_offload", "n_prompt", "n_gen", "n_depth", "test_time",
"avg_ns", "stddev_ns", "avg_ts", "stddev_ts",
};
return fields;
if (field == "build_number" || field == "n_batch" || field == "n_ubatch" || field == "n_threads" ||
field == "poll" || field == "model_size" || field == "model_n_params" || field == "n_gpu_layers" ||
field == "main_gpu" || field == "n_prompt" || field == "n_gen" || field == "n_depth" ||
- field == "avg_ns" || field == "stddev_ns") {
+ field == "avg_ns" || field == "stddev_ns" || field == "no_op_offload") {
return INT;
}
if (field == "f16_kv" || field == "no_kv_offload" || field == "cpu_strict" || field == "flash_attn" ||
tensor_buft_overrides_str,
std::to_string(use_mmap),
std::to_string(embeddings),
+ std::to_string(no_op_offload),
std::to_string(n_prompt),
std::to_string(n_gen),
std::to_string(n_depth),
if (field == "test") {
return 15;
}
+ if (field == "no_op_offload") {
+ return 4;
+ }
int width = std::max((int) field.length(), 10);
if (field == "embeddings") {
return "embd";
}
+ if (field == "no_op_offload") {
+ return "nopo";
+ }
if (field == "tensor_split") {
return "ts";
}
if (params.embeddings.size() > 1 || params.embeddings != cmd_params_defaults.embeddings) {
fields.emplace_back("embeddings");
}
+ if (params.no_op_offload.size() > 1 || params.no_op_offload != cmd_params_defaults.no_op_offload) {
+ fields.emplace_back("no_op_offload");
+ }
fields.emplace_back("test");
fields.emplace_back("t/s");