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llama : pad KV cache size #4280

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Merged
merged 2 commits into from
Dec 3, 2023
Merged

llama : pad KV cache size #4280

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3e68df8
llama : pad KV cache size to 32
ggerganov Dec 1, 2023
3cb1c34
metal : try to improve batched decoding
ggerganov Dec 1, 2023
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2 changes: 1 addition & 1 deletion ggml-metal.m
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Expand Up @@ -1083,7 +1083,7 @@ void ggml_metal_graph_compute(

// find the break-even point where the matrix-matrix kernel becomes more efficient compared
// to the matrix-vector kernel
int ne11_mm_min = 1;
int ne11_mm_min = src0t == GGML_TYPE_F16 ? 1 : 16;

#if 0
// the numbers below are measured on M2 Ultra for 7B and 13B models
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3 changes: 1 addition & 2 deletions llama.cpp
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Expand Up @@ -5744,8 +5744,7 @@ static int llama_decode_internal(
// a heuristic, to avoid attending the full cache if it is not yet utilized
// after enough generations, the benefit from this heuristic disappears
// if we start defragmenting the cache, the benefit from this will be more important
//kv_self.n = std::max(32, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32)); // TODO: this might be better for CUDA?
kv_self.n = std::min((int32_t) cparams.n_ctx, std::max(32, llama_kv_cache_cell_max(kv_self)));
kv_self.n = std::min((int32_t) cparams.n_ctx, std::max(32, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32)));

//printf("kv_self.n = %5d, kv_self.used = %5d, kv_self.head = %5d\n", kv_self.n, kv_self.used, kv_self.head);

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