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llama.cpp
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Refactor and combine lambdas

This commit is contained in:
Ed Addario 2025-08-22 16:57:58 +01:00
parent f05c8483d8
commit fea99d051a
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1 changed files with 13 additions and 27 deletions
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src/llama-quant.cpp
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@ -660,20 +660,6 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
GGML_TYPE_Q6_K GGML_TYPE_Q6_K
}; };
auto get_values = [&](const std::string & tensor_name) -> const float * {
if (!values_data) { return nullptr; }
const auto it = values_data->find(remap_imatrix(tensor_name, mapped));
if (it == values_data->end()) { return nullptr; }
return it->second.data();
};
auto get_activations = [&](const std::string & tensor_name) -> const float * {
if (!activations_data) { return nullptr; }
const auto it = activations_data->find(remap_imatrix(tensor_name, mapped));
if (it == activations_data->end()) { return nullptr; }
return it->second.data();
};
auto tensor_bytes = [](const ggml_tensor * t, const ggml_type typ) -> size_t { auto tensor_bytes = [](const ggml_tensor * t, const ggml_type typ) -> size_t {
const int64_t n_per_row = t->ne[0]; const int64_t n_per_row = t->ne[0];
const size_t row_sz = ggml_row_size(typ, n_per_row); const size_t row_sz = ggml_row_size(typ, n_per_row);
@ -991,6 +977,15 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
sample_rows_per_slice[slice] = current_sampled_rows; sample_rows_per_slice[slice] = current_sampled_rows;
} }
auto side_data = [&](const std::unordered_map<std::string, std::vector<float>> * m, const std::string & tensor_name) -> std::pair<const float*, size_t> {
if (!m) { return {nullptr, 0}; }
const std::string key = remap_imatrix(tensor_name, mapped);
const auto it = m->find(key);
if (it == m->end()) { return {nullptr, 0}; }
return { it->second.data(), it->second.size() };
};
// Copy this row's side data (values and activations), or broadcasts to all slices
auto copy_or_broadcast = [&](const float *src, size_t src_sz, std::vector<float> &dst) { auto copy_or_broadcast = [&](const float *src, size_t src_sz, std::vector<float> &dst) {
const size_t want = (size_t)ne2 * (size_t)n_per_row; const size_t want = (size_t)ne2 * (size_t)n_per_row;
dst.clear(); dst.clear();
@ -1005,26 +1000,17 @@ static std::unordered_map<std::string, ggml_type> target_bpw_type(
std::memcpy(dst.data() + s * n_per_row, src, n_per_row * sizeof(float)); std::memcpy(dst.data() + s * n_per_row, src, n_per_row * sizeof(float));
} }
} else { } else {
// Mismatch – safer to skip using it for this tensor
LLAMA_LOG_WARN("%s: side data size mismatch for %s: got %zu, expected %zu or %zu; ignoring\n", LLAMA_LOG_WARN("%s: side data size mismatch for %s: got %zu, expected %zu or %zu; ignoring\n",
__func__, name.c_str(), src_sz, (size_t)n_per_row, want); __func__, name.c_str(), src_sz, (size_t)n_per_row, want);
} }
}; };
const float * values_all = get_values(name); const auto [values_all, values_sz] = side_data(values_data, name);
const float * activations_all = get_activations(name); const auto [activations_all, activations_sz] = side_data(activations_data, name);
std::vector<float> values_sample; std::vector<float> values_sample;
std::vector<float> activations_sample; std::vector<float> activations_sample;
if (values_all) { if (values_all) { copy_or_broadcast(values_all, values_sz, values_sample); }
auto itv = values_data->find(remap_imatrix(name, mapped)); if (activations_all) { copy_or_broadcast(activations_all, activations_sz, activations_sample); }
const size_t sz = itv == values_data->end() ? 0 : itv->second.size();
copy_or_broadcast(values_all, sz, values_sample);
}
if (activations_all) {
auto ita = activations_data->find(remap_imatrix(name, mapped));
const size_t sz = ita == activations_data->end() ? 0 : ita->second.size();
copy_or_broadcast(activations_all, sz, activations_sample);
}
const int64_t nelem = ggml_nelements(t); const int64_t nelem = ggml_nelements(t);
tensor_info info; tensor_info info;