vbatts/llama.cpp
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llama.cpp : better memory usage prints with allocator

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slaren 2023-07-28 00:36:48 +02:00
parent 966c069b3f
commit cd4a8cd28c
1 changed files with 32 additions and 20 deletions
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52
llama.cpp
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@ -1252,12 +1252,16 @@ static void llama_model_load_internal(
const size_t scale = memory_type == GGML_TYPE_F32 ? 2 : 1;
// this is the total memory required to run the inference
const size_t mem_required =
size_t mem_required =
ctx_size +
mmapped_size - vram_weights + // weights in VRAM not in memory
mmapped_size - vram_weights; // weights in VRAM not in memory
#ifndef LLAMA_USE_ALLOCATOR
mem_required +=
MEM_REQ_SCRATCH0(hparams.n_ctx).at(model.type) +
MEM_REQ_SCRATCH1().at(model.type) +
MEM_REQ_EVAL().at(model.type);
#endif
// this is the memory required by one llama_state
const size_t mem_required_state =
@ -3272,30 +3276,38 @@ struct llama_context * llama_new_context_with_model(
}
#ifdef LLAMA_USE_ALLOCATOR
static const size_t tensor_alignment = 32;
ctx->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead());
{
static const size_t tensor_alignment = 32;
// the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data
ctx->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead());
// measure memory requirements for worst-case graph
ctx->alloc = ggml_allocator_new_measure(tensor_alignment);
// create measure allocator
ctx->alloc = ggml_allocator_new_measure(tensor_alignment);
// build worst-case graph
int n_tokens = std::min((int)hparams.n_ctx, params.n_batch);
int n_past = hparams.n_ctx - n_tokens;
llama_token token = llama_token_bos();
ggml_cgraph * gf = llama_build_graph(*ctx, &token, NULL, n_tokens, n_past);
// build worst-case graph
int n_tokens = std::min((int)hparams.n_ctx, params.n_batch);
int n_past = hparams.n_ctx - n_tokens;
llama_token token = llama_token_bos(); // not actually used by llama_build_graph, but required to choose between token and embedding inputs graph
ggml_cgraph * gf = llama_build_graph(*ctx, &token, NULL, n_tokens, n_past);
size_t size = ggml_allocator_alloc_graph_tensors(ctx->alloc, gf) + tensor_alignment;
fprintf(stderr, "%s: worst-case graph size = %7.2f MB\n", __func__, size / 1024.0 / 1024.0);
fprintf(stderr, "%s: compute buffer total size: %7.2f MB\n", __func__, (ctx->buf_compute.size + size) / 1024.0 / 1024.0);
// measure memory requirements for the graph
size_t alloc_size = ggml_allocator_alloc_graph_tensors(ctx->alloc, gf) + tensor_alignment;
size_t prev_req = MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type) + MEM_REQ_SCRATCH1().at(ctx->model.type) + MEM_REQ_EVAL().at(ctx->model.type);
fprintf(stderr, "%s: equivalent with scratch buffer: %7.2f MB\n", __func__, prev_req / 1024.0 / 1024.0);
fprintf(stderr, "%s: compute buffer total size = %7.2f MB\n", __func__, (ctx->buf_compute.size + alloc_size) / 1024.0 / 1024.0);
// debug - for comparison with scratch buffer
//size_t prev_req =
// MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type) +
// MEM_REQ_SCRATCH1().at(ctx->model.type) +
// MEM_REQ_EVAL().at(ctx->model.type);
//fprintf(stderr, "%s: (debug) equivalent with scratch buffer = %7.2f MB\n", __func__, prev_req / 1024.0 / 1024.0);
// recreate allocator with exact memory requirements
ggml_allocator_free(ctx->alloc);
ctx->buf_alloc.resize(size);
ctx->alloc = ggml_allocator_new(ctx->buf_alloc.addr, ctx->buf_alloc.size, tensor_alignment);
// recreate allocator with exact memory requirements
ggml_allocator_free(ctx->alloc);
ctx->buf_alloc.resize(alloc_size);
ctx->alloc = ggml_allocator_new(ctx->buf_alloc.addr, ctx->buf_alloc.size, tensor_alignment);
}
#else
ctx->buf_compute.resize(MEM_REQ_EVAL().at(ctx->model.type) + ggml_graph_overhead());
#endif