vbatts/llama.cpp
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update imatrix

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This commit is contained in:
slaren 2024-04-02 02:05:38 +02:00
parent deea2007b4
commit b4a62062db
1 changed files with 14 additions and 14 deletions
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28
examples/imatrix/imatrix.cpp
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@ -100,27 +100,27 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
if (t->op == GGML_OP_MUL_MAT_ID) { if (t->op == GGML_OP_MUL_MAT_ID) {
const int idx = ((int32_t *) t->op_params)[0]; const int idx = ((int32_t *) t->op_params)[0];
const int n_as = ((int32_t *) t->op_params)[1]; const ggml_tensor * ids = t->src[2];
const int n_as = src0->ne[2];
// the top-k selected expert ids are stored in the src0 tensor // the top-k selected expert ids are stored in the ids tensor
// for simplicity, always copy src0 to host, because it is small // for simplicity, always copy ids to host, because it is small
// take into account that src0 is not contiguous! // take into account that ids is not contiguous!
GGML_ASSERT(src0->ne[1] == src1->ne[1]); GGML_ASSERT(ids->ne[1] == src1->ne[1]);
GGML_ASSERT(n_as*ggml_nrows(src0)*sizeof(int) == GGML_PAD(ggml_nbytes(src0), n_as*sizeof(int))); GGML_ASSERT(n_as*ggml_nrows(ids)*sizeof(int) == GGML_PAD(ggml_nbytes(ids), n_as*sizeof(int)));
m_ids.resize(ggml_nbytes(src0)/sizeof(int)); m_ids.resize(ggml_nbytes(ids)/sizeof(int));
ggml_backend_tensor_get(src0, m_ids.data(), 0, ggml_nbytes(src0)); ggml_backend_tensor_get(ids, m_ids.data(), 0, ggml_nbytes(ids));
// loop over all possible experts, regardless if they are used or not in the batch // loop over all possible experts, regardless if they are used or not in the batch
// this is necessary to guarantee equal number of "ncall" for each tensor // this is necessary to guarantee equal number of "ncall" for each tensor
for (int ex = 0; ex < n_as; ++ex) { for (int ex = 0; ex < n_as; ++ex) {
src0 = t->src[2 + ex];
wname = filter_tensor_name(src0->name);
auto& e = m_stats[wname]; auto& e = m_stats[wname];
size_t e_start = ex*src1->ne[0];
if (e.values.empty()) { if (e.values.empty()) {
e.values.resize(src1->ne[0], 0); e.values.resize(src1->ne[0]*n_as, 0);
} }
else if (e.values.size() != (size_t)src1->ne[0]) { else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]); fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as);
exit(1); //GGML_ASSERT(false); exit(1); //GGML_ASSERT(false);
} }
// NOTE: since we select top-k experts, the number of calls for the expert tensors will be k times larger // NOTE: since we select top-k experts, the number of calls for the expert tensors will be k times larger
@ -136,7 +136,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
if (excur != ex) continue; if (excur != ex) continue;
const float * x = data + row * src1->ne[0]; const float * x = data + row * src1->ne[0];
for (int j = 0; j < (int)src1->ne[0]; ++j) { for (int j = 0; j < (int)src1->ne[0]; ++j) {
e.values[j] += x[j]*x[j]; e.values[e_start + j] += x[j]*x[j];
} }
} }
if (e.ncall > m_last_call) { if (e.ncall > m_last_call) {