vbatts/llama.cpp
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llama : add pipeline parallelism support (#6017)

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* llama : add pipeline parallelism support for batch processing with multiple CUDA GPUs

ggml-ci

* server : add -ub, --ubatch-size parameter

* fix server embedding test

* llama : fix Mamba inference for pipeline parallelism

Tested to work correctly with both `main` and `parallel` examples.

* llama : limit max batch size to n_batch

* add LLAMA_SCHED_MAX_COPIES to configure the number of input copies for pipeline parallelism
default increase to 4 (from 2)

changing this value may improve performance for some systems, but increases memory usage

* fix hip build

* fix sycl build (disable cpy_tensor_async)

* fix hip build

* llama : limit n_batch and n_ubatch to n_ctx during context creation

* llama : fix norm backend

* batched-bench : sync after decode

* swiftui : sync after decode

* ggml : allow ggml_get_rows to use multiple threads if they are available

* check n_ubatch >= n_tokens with non-casual attention

* llama : do not limit n_batch to n_ctx with non-casual attn

* server : construct batch with size of llama_n_batch

* ggml_backend_cpu_graph_compute : fix return value when alloc fails

* llama : better n_batch and n_ubatch comment

* fix merge

* small fix

* reduce default n_batch to 2048

---------

Co-authored-by: Francis Couture-Harpin <git@compilade.net>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
slaren 2024-03-13 18:54:21 +01:00 • committed by GitHub
parent d8fd0ccf6a
commit f30ea47a87
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25 changed files with 1467 additions and 887 deletions
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32
examples/server/server.cpp
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@ -147,7 +147,7 @@ struct server_slot {
int32_t n_decoded = 0;
int32_t n_remaining = -1;
int32_t i_batch = -1;
int32_t n_predict = -1;
int32_t n_predict = -1; // TODO: disambiguate from params.n_predict
int32_t n_prompt_tokens = 0;
int32_t n_prompt_tokens_processed = 0;
@ -739,7 +739,13 @@ struct server_context {
default_generation_settings_for_props = get_formated_generation(slots.front());
default_generation_settings_for_props["seed"] = -1;
batch = llama_batch_init(n_ctx, 0, params.n_parallel);
// the update_slots() logic will always submit a maximum of n_batch tokens
// note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used)
{
const int32_t n_batch = llama_n_batch(ctx);
batch = llama_batch_init(n_batch, 0, params.n_parallel);
}
metrics.init();
}
@ -1036,8 +1042,10 @@ struct server_context {
llama_batch_add(batch, system_tokens[i], i, { 0 }, false);
}
for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += params.n_batch) {
const int32_t n_tokens = std::min(params.n_batch, (int32_t) (batch.n_tokens - i));
const int32_t n_batch = llama_n_batch(ctx);
for (int32_t i = 0; i < batch.n_tokens; i += n_batch) {
const int32_t n_tokens = std::min(params.n_batch, batch.n_tokens - i);
llama_batch batch_view = {
n_tokens,
batch.token + i,
@ -1226,7 +1234,7 @@ struct server_context {
{"mirostat_eta", slot.sparams.mirostat_eta},
{"penalize_nl", slot.sparams.penalize_nl},
{"stop", slot.params.antiprompt},
{"n_predict", slot.params.n_predict},
{"n_predict", slot.params.n_predict}, // TODO: fix duplicate key n_predict
{"n_keep", params.n_keep},
{"ignore_eos", ignore_eos},
{"stream", slot.params.stream},
@ -1738,7 +1746,8 @@ struct server_context {
}
// process in chunks of params.n_batch
int32_t n_batch = params.n_batch;
int32_t n_batch = llama_n_batch(ctx);
int32_t n_ubatch = llama_n_ubatch(ctx);
// next, batch any pending prompts without exceeding n_batch
if (params.cont_batching || batch.n_tokens == 0) {
@ -1811,7 +1820,7 @@ struct server_context {
if (slot.embedding) {
// this prompt is too large to process - discard it
if (slot.n_prompt_tokens > n_batch) {
if (slot.n_prompt_tokens > n_ubatch) {
slot.state = SLOT_STATE_PROCESSING;
slot.command = SLOT_COMMAND_NONE;
slot.release();
@ -2157,7 +2166,8 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co
printf(" --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n");
printf(" -dt N, --defrag-thold N\n");
printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
printf(" -b N, --batch-size N logical maximum batch size (default: %d)\n", params.n_batch);
printf(" -ub N, --ubatch-size N physical maximum batch size (default: %d)\n", params.n_ubatch);
printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
printf(" not recommended: doubles context memory required and no measurable increase in quality\n");
if (llama_supports_mlock()) {
@ -2424,6 +2434,12 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams,
break;
}
params.n_batch = std::stoi(argv[i]);
} else if (arg == "-ub" || arg == "--ubatch-size") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.n_ubatch = std::stoi(argv[i]);
} else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") {
if (++i >= argc) {
invalid_param = true;