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perf : study batched decoding bottleneck #3726

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perf : study batched decoding bottleneck #3726

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2471d56
llama : profiling the attention compute
ggerganov Oct 22, 2023
ed9fde7
ggml : skip nops
ggerganov Oct 22, 2023
cb79f8a
llama : add SKIP_KQ_KQV option
ggerganov Oct 22, 2023
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4 changes: 4 additions & 0 deletions ggml.c
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Original file line number Diff line number Diff line change
Expand Up @@ -16602,6 +16602,10 @@ static void ggml_compute_forward_cross_entropy_loss_back(
static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
GGML_ASSERT(params);

if (tensor->op == GGML_OP_NONE) {
return;
}

#ifdef GGML_USE_CUBLAS
bool skip_cpu = ggml_cuda_compute_forward(params, tensor);
if (skip_cpu) {
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27 changes: 27 additions & 0 deletions llama.cpp
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Expand Up @@ -5815,6 +5815,33 @@ static struct ggml_cgraph * llama_build_graph(
GGML_ASSERT(false);
}

#if 1
for (int i = 0; i < result->n_nodes; ++i) {
struct ggml_tensor * node = result->nodes[i];
if (getenv("SKIP_KQ_ALL")) {
if (
strcmp(node->name, "KQ") == 0 ||
strcmp(node->name, "KQ_scaled") == 0 ||
strcmp(node->name, "KQ_masked") == 0 ||
strcmp(node->name, "KQ_soft_max") == 0 ||
strcmp(node->name, "KQV") == 0 ||
false) {
//printf("skipping %s\n", dst->name);
node->op = GGML_OP_NONE;
}
}
if (getenv("SKIP_KQ_KQV")) {
if (
strcmp(node->name, "KQ") == 0 ||
strcmp(node->name, "KQV") == 0 ||
false) {
//printf("skipping %s\n", dst->name);
node->op = GGML_OP_NONE;
}
}
}
#endif

return result;
}

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