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llama : enable GPU inference by default with Metal

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Georgi Gerganov 2023-09-03 10:30:53 +03:00
parent 15f1790a75
commit 99161230c4
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GPG Key ID: 449E073F9DC10735
5 changed files with 21 additions and 18 deletions
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4
common/common.cpp
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@ -702,7 +702,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
lparams.n_ctx = params.n_ctx;
lparams.n_batch = params.n_batch;
lparams.n_gpu_layers = params.n_gpu_layers;
lparams.n_gpu_layers = params.n_gpu_layers != -1 ? params.n_gpu_layers : lparams.n_gpu_layers;
lparams.main_gpu = params.main_gpu;
lparams.tensor_split = params.tensor_split;
lparams.low_vram = params.low_vram;
@ -1064,7 +1064,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str());
fprintf(stream, "mtest: %s # default: false\n", params.mem_test ? "true" : "false");
fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
fprintf(stream, "n_gpu_layers: %d # default: 0\n", params.n_gpu_layers);
fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", params.n_probs);
fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");

2
common/common.h
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@ -33,7 +33,7 @@ struct gpt_params {
int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
int32_t n_gpu_layers = 0; // number of layers to store in VRAM
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.

15
examples/main/main.cpp
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@ -151,14 +151,6 @@ int main(int argc, char ** argv) {
LOG_TEE("%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale);
}
if (params.n_ctx > 2048) {
// TODO: determine the actual max context of the model (e.g. 4096 for LLaMA v2) and use that instead of 2048
LOG_TEE("%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified)\n", __func__, params.n_ctx);
} else if (params.n_ctx < 8) {
LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
params.n_ctx = 8;
}
LOG_TEE("%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed == LLAMA_DEFAULT_SEED) {
@ -194,6 +186,13 @@ int main(int argc, char ** argv) {
return 1;
}
if (params.n_ctx > llama_n_ctx(ctx)) {
LOG_TEE("%s: warning: base model only supports context sizes no greater than %d tokens (%d specified)\n", __func__, llama_n_ctx(ctx), params.n_ctx);
} else if (params.n_ctx < 8) {
LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
params.n_ctx = 8;
}
// print system information
{
LOG_TEE("\n");

12
examples/perplexity/perplexity.cpp
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@ -368,7 +368,7 @@ results_perplexity perplexity(llama_context * ctx, const gpt_params & params) {
// Example, we have a context window of 512, we will compute perplexity for each of the
// last 256 tokens. Then, we split the input up into context window size chunks to
// process the entire prompt.
const int first = std::min(512, params.n_ctx/2);
const int first = params.n_ctx/2;
process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, params.n_ctx - 1 - first,
workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
count += params.n_ctx - first - 1;
@ -668,11 +668,6 @@ int main(int argc, char ** argv) {
params.n_ctx += params.ppl_stride/2;
}
if (params.n_ctx > 2048) {
fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
"expect poor results\n", __func__, params.n_ctx);
}
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed == LLAMA_DEFAULT_SEED) {
@ -698,6 +693,11 @@ int main(int argc, char ** argv) {
return 1;
}
if (params.n_ctx > llama_n_ctx(ctx)) {
fprintf(stderr, "%s: warning: model might not support context sizes greater than %d tokens (%d specified);"
"expect poor results\n", __func__, llama_n_ctx(ctx), params.n_ctx);
}
// print system information
{
fprintf(stderr, "\n");

6
llama.cpp
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@ -5334,7 +5334,7 @@ struct llama_context_params llama_context_default_params() {
/*.seed =*/ LLAMA_DEFAULT_SEED,
/*.n_ctx =*/ 512,
/*.n_batch =*/ 512,
/*.gpu_layers =*/ 0,
/*.n_gpu_layers =*/ 0,
/*.main_gpu =*/ 0,
/*.tensor_split =*/ nullptr,
/*.rope_freq_base =*/ 10000.0f,
@ -5351,6 +5351,10 @@ struct llama_context_params llama_context_default_params() {
/*.embedding =*/ false,
};
#ifdef GGML_USE_METAL
result.n_gpu_layers = 1;
#endif
return result;
}