diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index 1b537407f..f49bf9ec4 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -12,6 +12,7 @@ #include #include #include +#include #include #include #include @@ -29,15 +30,19 @@ static void print_usage(int, char ** argv) { LOG("\n"); } +static bool str_has_suffix(const std::string & str, const std::string & suffix) { + return str.size() >= suffix.size() && str.compare(str.size() - suffix.size(), str.size(), suffix) == 0; +} + static bool str_remove_suffix(std::string & str, const std::string & suffix) { - bool has_suffix = str.size() >= suffix.size() && str.compare(str.size() - suffix.size(), str.size(), suffix) == 0; + bool has_suffix = str_has_suffix(str, suffix); if (has_suffix) { str = str.substr(0, str.size() - suffix.size()); } return has_suffix; } -static const char * const LLM_KV_IMATRIX_DATASET = "imatrix.dataset"; +static const char * const LLM_KV_IMATRIX_DATASETS = "imatrix.datasets"; static const char * const LLM_KV_IMATRIX_CHUNK_COUNT = "imatrix.chunk_count"; static const char * const LLM_KV_IMATRIX_CHUNK_SIZE = "imatrix.chunk_size"; @@ -51,12 +56,15 @@ public: IMatrixCollector() = default; void set_params(common_params params) { m_params = std::move(params); } bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data); + void save_imatrix_legacy(int32_t ncall = -1) const; void save_imatrix(int32_t n_chunk = -1) const; + bool load_imatrix_legacy(const char * fname); bool load_imatrix(const char * file_name); private: std::unordered_map m_stats; common_params m_params; std::mutex m_mutex; + std::vector m_datasets; int32_t m_last_chunk = 0; std::vector m_src1_data; std::vector m_ids; // the expert ids from ggml_mul_mat_id @@ -88,6 +96,8 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * const struct ggml_tensor * src1 = t->src[1]; std::string wname = filter_tensor_name(src0->name); + const int32_t chunk_size = m_params.n_ctx / m_params.n_parallel; + // when ask is true, the scheduler wants to know if we are interested in data from this tensor // if we return true, a follow-up call will be made with ask=false in which we can do the actual collection if (ask) { @@ -175,7 +185,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * } } } - const int32_t n_chunk = e.counts[ex] / (m_params.n_ctx / m_params.n_parallel); + const int32_t n_chunk = e.counts[ex] / chunk_size; if (n_chunk > m_last_chunk) { const int32_t chunk_step = n_chunk - m_last_chunk; m_last_chunk = n_chunk; @@ -214,7 +224,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * } } } - const int32_t n_chunk = e.counts[0] / (m_params.n_ctx / m_params.n_parallel); + const int32_t n_chunk = e.counts[0] / chunk_size; if (n_chunk > m_last_chunk) { const int32_t chunk_step = n_chunk - m_last_chunk; m_last_chunk = n_chunk; @@ -230,19 +240,19 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * return true; } -void IMatrixCollector::save_imatrix(int32_t n_chunk) const { +void IMatrixCollector::save_imatrix_legacy(int32_t ncall) const { auto fname = m_params.out_file; - if (n_chunk > 0) { + if (ncall > 0) { fname += ".at_"; - fname += std::to_string(n_chunk); + fname += std::to_string(ncall); } // avoid writing imatrix entries that do not have full data // this can happen with MoE models where some of the experts end up not being exercised by the provided training data + int n_entries = 0; std::vector to_store; - size_t data_size = 0; bool is_first = true; // for printing for (const auto & kv : m_stats) { @@ -274,9 +284,8 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const { continue; } + n_entries++; to_store.push_back(kv.first); - data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.values.size(), GGML_MEM_ALIGN); - data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.counts.size(), GGML_MEM_ALIGN); } if (to_store.size() < m_stats.size()) { @@ -286,6 +295,79 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const { // deterministic tensor name order std::sort(to_store.begin(), to_store.end()); + const int32_t chunk_size = m_params.n_ctx / m_params.n_parallel; + + std::ofstream out(fname, std::ios::binary); + out.write((const char *) &n_entries, sizeof(n_entries)); + for (const auto & name : to_store) { + const auto & stat = m_stats.at(name); + const int32_t len = name.size(); + out.write((const char *) &len, sizeof(len)); + out.write(name.c_str(), len); + const int32_t ncall = *std::max_element(stat.counts.begin(), stat.counts.end()) / chunk_size; + out.write((const char *) &ncall, sizeof(ncall)); + const int32_t nval = stat.values.size(); + const int32_t nmat = stat.counts.size(); + out.write((const char *) &nval, sizeof(nval)); + if (nval > 0 && nmat > 0) { + std::vector tmp(nval); + for (int32_t i = 0; i < nval; i++) { + const float counts = static_cast(stat.counts[i / (nval / nmat)]); + tmp[i] = (stat.values[i] / counts) * static_cast(ncall); + } + out.write((const char *) tmp.data(), nval * sizeof(float)); + } + } + + // Write the number of call the matrix was computed with + out.write((const char *) &m_last_chunk, sizeof(m_last_chunk)); + + // Write the input filename at the end of the file to later on specify it in quantize + { + const char * dataset_file = m_params.prompt_file.c_str(); + int32_t len = m_params.prompt_file.size(); + // When there is no prompt but there were other imatrix files loaded, use the last dataset + if (m_params.prompt_file.empty() && !m_datasets.empty()) { + const std::string & dataset_str = m_datasets[m_datasets.size() - 1]; + dataset_file = dataset_str.c_str(); + len = dataset_str.size(); + } + out.write((const char *) &len, sizeof(len)); + out.write(dataset_file, len); + } + + LOGV(1, "\n"); + LOG_DBGV(1, "%s: stored collected data after %d chunks in %s\n", __func__, m_last_chunk, fname.c_str()); +} + +void IMatrixCollector::save_imatrix(int32_t n_chunk) const { + auto fname = m_params.out_file; + + // TODO: use the new format by default also for .imatrix + if (!str_has_suffix(fname, ".gguf")) { + return this->save_imatrix_legacy(n_chunk); + } + + if (n_chunk > 0) { + fname += ".at_"; + fname += std::to_string(n_chunk); + } + + // write imatrix entries even if they don't have full data. (can be corrected when reading) + // this can happen with MoE models where some of the experts end up not being exercised by the provided training data + + std::vector to_store; + size_t data_size = 0; + + for (const auto & kv : m_stats) { + to_store.push_back(kv.first); + data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.values.size(), GGML_MEM_ALIGN); + data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.counts.size(), GGML_MEM_ALIGN); + } + + // deterministic tensor name order + std::sort(to_store.begin(), to_store.end()); + struct ggml_init_params params = { /* .mem_size = */ data_size, /* .mem_buffer = */ NULL, @@ -294,31 +376,42 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const { struct ggml_context * ctx = ggml_init(params); struct gguf_context * ctx_gguf = gguf_init_empty(); - gguf_set_val_str(ctx_gguf, "general.type", "imatrix"); - // Write the input filename to later on specify it in quantize - gguf_set_val_str(ctx_gguf, LLM_KV_IMATRIX_DATASET, m_params.prompt_file.c_str()); - // Write the number of chunks the matrix was computed with - gguf_set_val_u32(ctx_gguf, LLM_KV_IMATRIX_CHUNK_COUNT, m_last_chunk); - gguf_set_val_u32(ctx_gguf, LLM_KV_IMATRIX_CHUNK_SIZE, m_params.n_ctx / m_params.n_parallel); + { + std::vector datasets; + datasets.reserve(m_datasets.size() + 1); + for (size_t i = 0; i < m_datasets.size(); ++i) { + datasets.push_back(m_datasets[i].c_str()); + } + if (!m_params.prompt_file.empty()) { + datasets.push_back(m_params.prompt_file.c_str()); + } + + gguf_set_val_str(ctx_gguf, "general.type", "imatrix"); + // Write the dataset paths + gguf_set_arr_str(ctx_gguf, LLM_KV_IMATRIX_DATASETS, datasets.data(), datasets.size()); + // Write the number of chunks the matrix was computed with + gguf_set_val_u32(ctx_gguf, LLM_KV_IMATRIX_CHUNK_COUNT, m_last_chunk); + gguf_set_val_u32(ctx_gguf, LLM_KV_IMATRIX_CHUNK_SIZE, m_params.n_ctx / m_params.n_parallel); + } for (const auto & name : to_store) { const auto & stat = m_stats.at(name); const int32_t nval = (int32_t) stat.values.size(); const int32_t nmat = (int32_t) stat.counts.size(); - if (nval > 0) { - struct ggml_tensor * sums = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nval / nmat, nmat); - struct ggml_tensor * counts = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, nmat); - ggml_format_name(sums, "%s.sums", name.c_str()); + if (nval > 0 && nmat > 0) { + struct ggml_tensor * in_sum2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nval / nmat, nmat); + struct ggml_tensor * counts = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, nmat); + ggml_format_name(in_sum2, "%s.in_sum2", name.c_str()); ggml_format_name(counts, "%s.counts", name.c_str()); for (int32_t j = 0; j < nval; ++j) { - ((float *) sums->data)[j] = (float) stat.values[j]; + ((float *) in_sum2->data)[j] = (float) stat.values[j]; } for (int32_t j = 0; j < nmat; ++j) { ((float *) counts->data)[j] = (float) stat.counts[j]; } - gguf_add_tensor(ctx_gguf, sums); + gguf_add_tensor(ctx_gguf, in_sum2); gguf_add_tensor(ctx_gguf, counts); } } @@ -332,6 +425,105 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const { ggml_free(ctx); } +bool IMatrixCollector::load_imatrix_legacy(const char * fname) { + std::ifstream in(fname, std::ios::binary); + if (!in) { + LOG_ERR("%s: failed to open %s\n", __func__, fname); + return false; + } + int n_entries; + in.read((char *) &n_entries, sizeof(n_entries)); + if (in.fail() || n_entries < 1) { + LOG_ERR("%s: no data in file %s\n", __func__, fname); + return false; + } + // Guess the chunk size because it's not stored in the file + const int32_t chunk_size = m_params.n_ctx / m_params.n_parallel; + + for (int i = 0; i < n_entries; ++i) { + int32_t len = 0; + in.read((char *) &len, sizeof(len)); + std::vector name_as_vec(len + 1); + in.read((char *) name_as_vec.data(), len); + if (in.fail()) { + LOG_ERR("%s: failed reading name for entry %d from %s\n", __func__, i + 1, fname); + return false; + } + name_as_vec[len] = 0; + std::string name{ name_as_vec.data() }; + auto & e = m_stats[std::move(name)]; + int32_t ncall = 0; + in.read((char *) &ncall, sizeof(ncall)); + int32_t nval = 0; + in.read((char *) &nval, sizeof(nval)); + if (in.fail() || nval < 1) { + LOG_ERR("%s: failed reading number of values for entry %d\n", __func__, i); + m_stats = {}; + return false; + } + + if (e.values.empty()) { + e.values.resize(nval, 0.0f); + e.counts.resize(1, 0); + } + + std::vector tmp(nval); + in.read((char *) tmp.data(), nval * sizeof(float)); + if (in.fail()) { + LOG_ERR("%s: failed reading data for entry %d\n", __func__, i); + m_stats = {}; + return false; + } + + // Recreate the state as expected by save_imatrix(), and correct for weighted sum. + for (int i = 0; i < nval; i++) { + e.values[i] += tmp[i] * chunk_size; + } + // The legacy format doesn't distinguish the counts for different experts + for (size_t j = 0; j < e.counts.size(); ++j) { + e.counts[j] += ncall * chunk_size; + } + } + + { + // TODO: extract into its own method; this is also used by the GGUF-based format + // Calculate the last chunk count + int64_t max_count = 0; + for (const auto & stats : m_stats) { + for (int64_t count : stats.second.counts) { + if (count > max_count) { + max_count = count; + } + } + } + m_last_chunk = max_count / (chunk_size); + } + + { + // Read the number of calls the matrix was computed with + int32_t n_calls; + in.read((char *) &n_calls, sizeof(n_calls)); + // ignore it because it's not important + } + + // Read the dataset path to include it when writing to GGUF + if (!in.fail()){ + int32_t len = 0; + in.read((char *) &len, sizeof(len)); + if (!in.fail()) { + std::vector dataset; + dataset.resize(len + 1, 0); + in.read(dataset.data(), len); + if (!in.fail()) { + m_datasets.push_back(dataset.data()); + } + } + } + + return true; +} + +// Using GGUF as the file format, for greater extensibility bool IMatrixCollector::load_imatrix(const char * file_name) { struct ggml_context * ctx = nullptr; struct gguf_init_params meta_gguf_params = { @@ -340,7 +532,7 @@ bool IMatrixCollector::load_imatrix(const char * file_name) { }; struct gguf_context * ctx_gguf = gguf_init_from_file(file_name, meta_gguf_params); if (!ctx_gguf) { - return false; + return this->load_imatrix_legacy(file_name); } const int32_t n_entries = gguf_get_n_tensors(ctx_gguf); if (n_entries < 1) { @@ -350,8 +542,17 @@ bool IMatrixCollector::load_imatrix(const char * file_name) { return false; } - const std::string sums_suffix{".sums"}; - const std::string counts_suffix{".counts"}; + const int64_t datasets_key = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_DATASETS); + if (datasets_key != -1 && gguf_get_arr_type(ctx_gguf, datasets_key) == GGUF_TYPE_STRING) { + const int64_t n = gguf_get_arr_n(ctx_gguf, datasets_key); + m_datasets.reserve(m_datasets.size() + n); + for (int64_t i = 0; i < n; ++i) { + m_datasets.push_back(gguf_get_arr_str(ctx_gguf, datasets_key, i)); + } + } + + const std::string in_sum2_suffix{ ".in_sum2" }; + const std::string counts_suffix{ ".counts" }; // Could re-use m_stats instead, but this allows // checking for completeness of *each* loaded imatrix file @@ -364,26 +565,23 @@ bool IMatrixCollector::load_imatrix(const char * file_name) { if (name.empty()) { continue; } - if (str_remove_suffix(name, sums_suffix)) { - // sums - sums_counts_for[name].first = cur; + if (str_remove_suffix(name, in_sum2_suffix)) { + // in_sum2 + sums_counts_for[std::move(name)].first = cur; } else if (str_remove_suffix(name, counts_suffix)) { // counts - sums_counts_for[name].second = cur; + sums_counts_for[std::move(name)].second = cur; } else { - LOG_ERR("%s: invalid imatrix tensor name: %s\n", __func__, name.c_str()); - gguf_free(ctx_gguf); - ggml_free(ctx); - return false; + // ignore other tensors } } for (const auto & sc : sums_counts_for) { - const std::string & name = sc.first; - const struct ggml_tensor * sums = sc.second.first; - const struct ggml_tensor * counts = sc.second.second; + const std::string & name = sc.first; + const struct ggml_tensor * in_sum2 = sc.second.first; + const struct ggml_tensor * counts = sc.second.second; - if (!sums || !counts) { + if (!in_sum2 || !counts) { LOG_ERR("%s: mismatched sums and counts for %s\n", __func__, name.c_str()); gguf_free(ctx_gguf); ggml_free(ctx); @@ -392,9 +590,9 @@ bool IMatrixCollector::load_imatrix(const char * file_name) { auto & e = m_stats[name]; - int64_t nval = ggml_nelements(sums); + int64_t nval = ggml_nelements(in_sum2); if (e.values.empty()) { - e.values.resize(nval, 0); + e.values.resize(nval, 0.0f); } else if ((size_t) nval != e.values.size()) { LOG_ERR("%s: mismatched sums size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) nval, e.values.size()); gguf_free(ctx_gguf); @@ -417,12 +615,25 @@ bool IMatrixCollector::load_imatrix(const char * file_name) { // Recreate the state as expected by save_imatrix() for (int64_t j = 0; j < nval; j++) { - e.values[j] += ((const float *) sums->data)[j]; + e.values[j] += ((const float *) in_sum2->data)[j]; } for (int64_t j = 0; j < ncounts; j++) { e.counts[j] += std::lround(((const float *) counts->data)[j]); } } + + // TODO: extract into its own method; this is also used by the legacy format + // Calculate the last chunk count + int64_t max_count = 0; + for (const auto & stats : m_stats) { + for (int64_t count : stats.second.counts) { + if (count > max_count) { + max_count = count; + } + } + } + m_last_chunk = max_count / (m_params.n_ctx / m_params.n_parallel); + gguf_free(ctx_gguf); ggml_free(ctx); return true; @@ -685,7 +896,7 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params, c int main(int argc, char ** argv) { common_params params; - params.out_file = "imatrix.dat" ; + params.out_file = "imatrix.gguf" ; params.n_ctx = 512; params.logits_all = true; diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 7f2afe657..1a37cf316 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -64,7 +64,7 @@ static const char * const LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES = "quantize.imatrix static const char * const LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS = "quantize.imatrix.chunks_count"; // TODO: share with imatrix.cpp -static const char * const LLM_KV_IMATRIX_DATASET = "imatrix.dataset"; +static const char * const LLM_KV_IMATRIX_DATASETS = "imatrix.datasets"; static const char * const LLM_KV_IMATRIX_CHUNK_COUNT = "imatrix.chunk_count"; static const char * const LLM_KV_IMATRIX_CHUNK_SIZE = "imatrix.chunk_size"; @@ -84,7 +84,7 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp for (auto ch : ftype_str_in) { ftype_str.push_back(std::toupper(ch)); } - for (auto & it : QUANT_OPTIONS) { + for (const auto & it : QUANT_OPTIONS) { if (striequals(it.name.c_str(), ftype_str.c_str())) { ftype = it.ftype; ftype_str_out = it.name; @@ -93,7 +93,7 @@ static bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftyp } try { int ftype_int = std::stoi(ftype_str); - for (auto & it : QUANT_OPTIONS) { + for (const auto & it : QUANT_OPTIONS) { if (it.ftype == ftype_int) { ftype = it.ftype; ftype_str_out = it.name; @@ -126,7 +126,7 @@ static void usage(const char * executable) { printf(" Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n"); printf("Note: --include-weights and --exclude-weights cannot be used together\n"); printf("\nAllowed quantization types:\n"); - for (auto & it : QUANT_OPTIONS) { + for (const auto & it : QUANT_OPTIONS) { if (it.name != "COPY") { printf(" %2d or ", it.ftype); } else { @@ -146,7 +146,71 @@ static bool str_remove_suffix(std::string & str, const std::string & suffix) { return has_suffix; } -static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_dataset, std::unordered_map> & imatrix_data) { +static int load_legacy_imatrix(const std::string & imatrix_file, std::vector & imatrix_datasets, std::unordered_map> & imatrix_data) { + std::ifstream in(imatrix_file.c_str(), std::ios::binary); + if (!in) { + printf("%s: failed to open %s\n",__func__, imatrix_file.c_str()); + exit(1); + } + int n_entries; + in.read((char *)&n_entries, sizeof(n_entries)); + if (in.fail() || n_entries < 1) { + printf("%s: no data in file %s\n", __func__, imatrix_file.c_str()); + exit(1); + } + for (int i = 0; i < n_entries; ++i) { + int len; in.read((char *)&len, sizeof(len)); + std::vector name_as_vec(len+1); + in.read((char *)name_as_vec.data(), len); + if (in.fail()) { + printf("%s: failed reading name for entry %d from %s\n", __func__, i+1, imatrix_file.c_str()); + exit(1); + } + name_as_vec[len] = 0; + std::string name{name_as_vec.data()}; + auto & e = imatrix_data[name]; + int ncall; + in.read((char *)&ncall, sizeof(ncall)); + int nval; + in.read((char *)&nval, sizeof(nval)); + if (in.fail() || nval < 1) { + printf("%s: failed reading number of values for entry %d\n", __func__, i); + imatrix_data = {}; + exit(1); + } + e.resize(nval); + in.read((char *)e.data(), nval*sizeof(float)); + if (in.fail()) { + printf("%s: failed reading data for entry %d\n", __func__, i); + imatrix_data = {}; + exit(1); + } + if (ncall > 0) { + for (auto& v : e) v /= ncall; + } + + if (getenv("LLAMA_TRACE")) { + printf("%s: loaded data (size = %6d, ncall = %6d) for '%s'\n", __func__, int(e.size()), ncall, name.c_str()); + } + } + + // latest imatrix version contains the dataset filename at the end of the file + int m_last_call = 0; + if (in.peek() != EOF) { + in.read((char *)&m_last_call, sizeof(m_last_call)); + int dataset_len; + in.read((char *)&dataset_len, sizeof(dataset_len)); + std::vector dataset_as_vec(dataset_len); + in.read(dataset_as_vec.data(), dataset_len); + imatrix_datasets.resize(1); + imatrix_datasets[0].assign(dataset_as_vec.begin(), dataset_as_vec.end()); + printf("%s: imatrix dataset='%s'\n", __func__, imatrix_datasets[0].c_str()); + } + printf("%s: loaded %d importance matrix entries from %s computed on %d chunks\n", __func__, int(imatrix_data.size()), imatrix_file.c_str(), m_last_call); + return m_last_call; +} + +static int load_imatrix(const std::string & imatrix_file, std::vector & imatrix_datasets, std::unordered_map> & imatrix_data) { struct ggml_context * ctx = nullptr; struct gguf_init_params meta_gguf_params = { @@ -155,8 +219,8 @@ static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_ }; struct gguf_context * ctx_gguf = gguf_init_from_file(imatrix_file.c_str(), meta_gguf_params); if (!ctx_gguf) { - fprintf(stderr, "%s: if this is an older imatrix file, make sure to convert it to the GGUF-based imatrix format\n", __func__); - exit(1); + fprintf(stderr, "%s: imatrix file '%s' is using old format\n", __func__, imatrix_file.c_str()); + return load_legacy_imatrix(imatrix_file, imatrix_datasets, imatrix_data); } const int32_t n_entries = gguf_get_n_tensors(ctx_gguf); if (n_entries < 1) { @@ -166,7 +230,7 @@ static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_ exit(1); } - const int dataset_idx = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_DATASET); + const int dataset_idx = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_DATASETS); const int chunk_count_idx = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_COUNT); const int chunk_size_idx = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_SIZE); if (dataset_idx < 0 || chunk_count_idx < 0 || chunk_size_idx < 0) { @@ -178,8 +242,8 @@ static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_ const uint32_t chunk_size = gguf_get_val_u32(ctx_gguf, chunk_size_idx); - const std::string sums_suffix{".sums"}; - const std::string counts_suffix{".counts"}; + const std::string sums_suffix{ ".in_sum2" }; + const std::string counts_suffix{ ".counts" }; // Using an ordered map to get a deterministic iteration order. std::map> sums_counts_for; @@ -190,16 +254,13 @@ static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_ if (name.empty()) { continue; } if (str_remove_suffix(name, sums_suffix)) { - // sums - sums_counts_for[name].first = cur; + // in_sum2 + sums_counts_for[std::move(name)].first = cur; } else if (str_remove_suffix(name, counts_suffix)) { // counts - sums_counts_for[name].second = cur; + sums_counts_for[std::move(name)].second = cur; } else { - fprintf(stderr, "%s: invalid imatrix tensor name: %s\n", __func__, name.c_str()); - gguf_free(ctx_gguf); - ggml_free(ctx); - exit(1); + // ignore other tensors } } @@ -223,8 +284,15 @@ static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_ float max_count = 0.0f; for (int64_t j = 0; j < ne1; ++j) { const float count = ((const float *) counts->data)[j]; - for (int64_t i = 0; i < ne0; ++i) { - e[j*ne0 + i] = ((const float *) sums->data)[j*ne0 + i] / count; + if (count > 0.0f) { + for (int64_t i = 0; i < ne0; ++i) { + e[j*ne0 + i] = ((const float *) sums->data)[j*ne0 + i] / count; + } + } else { + // Partial imatrix data, this tensor never got any input during calibration + for (int64_t i = 0; i < ne0; ++i) { + e[j*ne0 + i] = 1; + } } if (count > max_count) { max_count = count; @@ -236,9 +304,18 @@ static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_ } int m_last_chunk = gguf_get_val_u32(ctx_gguf, chunk_count_idx); - imatrix_dataset = gguf_get_val_str(ctx_gguf, dataset_idx); - printf("%s: imatrix dataset='%s'\n", __func__, imatrix_dataset.c_str()); + int64_t n_datasets = gguf_get_arr_n(ctx_gguf, dataset_idx); + imatrix_datasets.resize(n_datasets); + for (int64_t i = 0; i < n_datasets; ++i) { + imatrix_datasets.push_back(gguf_get_val_str(ctx_gguf, dataset_idx)); + } + printf("%s: imatrix datasets=['%s'", __func__, imatrix_datasets[0].c_str()); + for (size_t i = 1; i < imatrix_datasets.size(); ++i) { + printf(", '%s'", imatrix_datasets[i].c_str()); + } + printf("]\n"); + printf("%s: loaded %d importance matrix entries from %s computed on %d chunks\n", __func__, int(imatrix_data.size()), imatrix_file.c_str(), m_last_chunk); gguf_free(ctx_gguf); @@ -248,7 +325,7 @@ static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_ } static int prepare_imatrix(const std::string & imatrix_file, - std::string & imatrix_dataset, + std::vector & imatrix_dataset, const std::vector & included_weights, const std::vector & excluded_weights, std::unordered_map> & imatrix_data) { @@ -260,18 +337,21 @@ static int prepare_imatrix(const std::string & imatrix_file, return m_last_call; } if (!excluded_weights.empty()) { - for (auto& name : excluded_weights) { - for (auto it = imatrix_data.begin(); it != imatrix_data.end(); ) { + for (const auto & name : excluded_weights) { + for (auto it = imatrix_data.begin(); it != imatrix_data.end();) { auto pos = it->first.find(name); - if (pos != std::string::npos) it = imatrix_data.erase(it); - else ++it; + if (pos != std::string::npos) { + it = imatrix_data.erase(it); + } else { + ++it; + } } } } if (!included_weights.empty()) { std::unordered_map> tmp; - for (auto& name : included_weights) { - for (auto& e : imatrix_data) { + for (const auto & name : included_weights) { + for (auto & e : imatrix_data) { auto pos = e.first.find(name); if (pos != std::string::npos) { tmp.emplace(std::move(e)); @@ -372,9 +452,9 @@ int main(int argc, char ** argv) { usage(argv[0]); } - std::string imatrix_dataset; + std::vector imatrix_datasets; std::unordered_map> imatrix_data; - int m_last_call = prepare_imatrix(imatrix_file, imatrix_dataset, included_weights, excluded_weights, imatrix_data); + int m_last_call = prepare_imatrix(imatrix_file, imatrix_datasets, included_weights, excluded_weights, imatrix_data); if (!imatrix_data.empty()) { params.imatrix = &imatrix_data; { @@ -385,11 +465,12 @@ int main(int argc, char ** argv) { kvo.val_str[127] = '\0'; kv_overrides.emplace_back(std::move(kvo)); } - if (!imatrix_dataset.empty()) { + if (!imatrix_datasets.empty()) { llama_model_kv_override kvo; + // TODO: list multiple datasets when there are more than one std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_DATASET); kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR; - strncpy(kvo.val_str, imatrix_dataset.c_str(), 127); + strncpy(kvo.val_str, imatrix_datasets[0].c_str(), 127); kvo.val_str[127] = '\0'; kv_overrides.emplace_back(std::move(kvo)); }