diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index 31b675e8f90b9..fdbc97a5e1513 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -2,6 +2,7 @@ #include "common.h" #include "log.h" #include "llama.h" +#include "gguf.h" #include #include @@ -13,6 +14,7 @@ #include #include #include +#include #include #if defined(_MSC_VER) @@ -22,16 +24,31 @@ static void print_usage(int, char ** argv) { LOG("\nexample usage:\n"); LOG("\n %s \\\n" - " -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] \\\n" + " -m model.gguf -f some-text.txt [-o imatrix.gguf] [--process-output] \\\n" " [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \\\n" - " [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]\n" , argv[0]); + " [--in-file imatrix-prev-0.gguf --in-file imatrix-prev-1.gguf ...]\n" , argv[0]); 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_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_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"; + struct Stats { - std::vector values; - std::vector counts; - int ncall = 0; + std::vector values; + std::vector counts; }; class IMatrixCollector { @@ -39,13 +56,16 @@ class IMatrixCollector { 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(int ncall = -1) const; - bool load_imatrix(const char * fname); + 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; - int m_last_call = 0; + 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 }; @@ -76,6 +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) { @@ -119,17 +141,23 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * auto & e = m_stats[wname]; - ++e.ncall; - + if (e.counts.size() == 1 && n_as > 1) { + // broadcast, when loading an old imatrix + e.counts.resize(n_as, e.counts[0]); + } if (e.values.empty()) { e.values.resize(src1->ne[0]*n_as, 0); - e.counts.resize(src1->ne[0]*n_as, 0); + e.counts.resize(n_as, 0); } else if (e.values.size() != (size_t)src1->ne[0]*n_as) { LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as); exit(1); //GGML_ABORT("fatal error"); } - LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[2], (int)src1->type); + else if (e.counts.size() != (size_t)n_as) { + LOG_ERR("%s: inconsistent expert count for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.counts.size(), (int)n_as); + exit(1); //GGML_ABORT("fatal error"); + } + LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_chunk, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[2], (int)src1->type); // loop over all possible experts, regardless if they are used or not in the batch for (int ex = 0; ex < n_as; ++ex) { size_t e_start = ex*src1->ne[0]; @@ -146,24 +174,26 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * const int64_t i12 = row; const float * x = (const float *)((const char *)data + i11*src1->nb[1] + i12*src1->nb[2]); + e.counts[ex]++; + for (int j = 0; j < (int)src1->ne[0]; ++j) { - e.values[e_start + j] += x[j]*x[j]; - e.counts[e_start + j]++; - if (!std::isfinite(e.values[e_start + j])) { - LOG("\n"); - LOG_ERR("%f detected in %s\n", e.values[e_start + j], wname.c_str()); + e.values[e_start + j] = std::fma(x[j], x[j], e.values[e_start + j]); + if (!std::isfinite((float)e.values[e_start + j])) { + LOG_ERR("%f detected in %s\n", (float)e.values[e_start + j], wname.c_str()); exit(1); } } } } - if (e.ncall > m_last_call) { - m_last_call = e.ncall; - if (m_last_call % m_params.n_out_freq == 0) { + 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; + if ((m_last_chunk % m_params.n_out_freq) / chunk_step == 0) { save_imatrix(); } - if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) { - save_imatrix(m_last_call); + if (m_params.n_save_freq > 0 && (m_last_chunk % m_params.n_save_freq) / chunk_step == 0) { + save_imatrix(m_last_chunk); } } } @@ -171,32 +201,38 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * auto & e = m_stats[wname]; if (e.values.empty()) { e.values.resize(src1->ne[0], 0); - e.counts.resize(src1->ne[0], 0); + e.counts.resize(1, 0); } else if (e.values.size() != (size_t)src1->ne[0]) { LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)src1->ne[0]); exit(1); //GGML_ABORT("fatal error"); } - ++e.ncall; - LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type); + else if (e.counts.size() != 1) { + LOG_ERR("%s: inconsistent expert count for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.counts.size(), 1); + exit(1); //GGML_ABORT("fatal error"); + } + LOG_DBGV(2, "%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_chunk, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type); + // TODO: higher dimensions for (int row = 0; row < (int)src1->ne[1]; ++row) { const float * x = data + row * src1->ne[0]; + e.counts[0]++; for (int j = 0; j < (int)src1->ne[0]; ++j) { - e.values[j] += x[j]*x[j]; - e.counts[j]++; - if (!std::isfinite(e.values[j])) { - LOG_ERR("%f detected in %s\n", e.values[j], wname.c_str()); + e.values[j] = std::fma(x[j], x[j], e.values[j]); + if (!std::isfinite((float)e.values[j])) { + LOG_ERR("%f detected in %s\n", (float)e.values[j], wname.c_str()); exit(1); } } } - if (e.ncall > m_last_call) { - m_last_call = e.ncall; - if (m_last_call % m_params.n_out_freq == 0) { + 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; + if ((m_last_chunk % m_params.n_out_freq) / chunk_step == 0) { save_imatrix(); } - if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) { - save_imatrix(m_last_call); + if (m_params.n_save_freq > 0 && (m_last_chunk % m_params.n_save_freq) / chunk_step == 0) { + save_imatrix(m_last_chunk); } } } @@ -204,7 +240,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * return true; } -void IMatrixCollector::save_imatrix(int ncall) const { +void IMatrixCollector::save_imatrix_legacy(int32_t ncall) const { auto fname = m_params.out_file; if (ncall > 0) { @@ -256,93 +292,350 @@ void IMatrixCollector::save_imatrix(int ncall) const { LOG_WRN("%s: storing only %zu out of %zu entries\n", __func__, to_store.size(), m_stats.size()); } + // 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); - int len = name.size(); + const int32_t len = name.size(); out.write((const char *) &len, sizeof(len)); out.write(name.c_str(), len); - out.write((const char *) &stat.ncall, sizeof(stat.ncall)); - int nval = stat.values.size(); + 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) { + if (nval > 0 && nmat > 0) { std::vector tmp(nval); - for (int i = 0; i < nval; i++) { - tmp[i] = (stat.values[i] / static_cast(stat.counts[i])) * static_cast(stat.ncall); + 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)); + 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_call, sizeof(m_last_call)); + 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 { - int len = m_params.prompt_file.size(); + 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(m_params.prompt_file.c_str(), 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, + /* .no_alloc = */ false, + }; + struct ggml_context * ctx = ggml_init(params); + struct gguf_context * ctx_gguf = gguf_init_empty(); + + { + 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 && 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 *) 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, in_sum2); + gguf_add_tensor(ctx_gguf, counts); + } } + gguf_write_to_file(ctx_gguf, fname.c_str(), false); + LOGV(1, "\n"); - LOG_DBGV(1, "%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname.c_str()); + LOG_DBGV(1, "%s: stored collected data after %d chunks in %s\n", __func__, m_last_chunk, fname.c_str()); + + gguf_free(ctx_gguf); + ggml_free(ctx); } -bool IMatrixCollector::load_imatrix(const char * fname) { +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); + LOG_ERR("%s: failed to open %s\n", __func__, fname); return false; } int n_entries; - in.read((char*)&n_entries, sizeof(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) { - int len; in.read((char *)&len, sizeof(len)); - std::vector name_as_vec(len+1); - in.read((char *)name_as_vec.data(), len); + 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); + 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()}; + std::string name{ name_as_vec.data() }; auto & e = m_stats[std::move(name)]; - int ncall; - in.read((char*)&ncall, sizeof(ncall)); - int nval; - in.read((char *)&nval, sizeof(nval)); + 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); + 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); - e.counts.resize(nval, 0); + e.values.resize(nval, 0.0f); + e.counts.resize(1, 0); } std::vector tmp(nval); - in.read((char*)tmp.data(), nval*sizeof(float)); + in.read((char *) tmp.data(), nval * sizeof(float)); if (in.fail()) { - LOG_ERR("%s: failed reading data for entry %d\n",__func__,i); + 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 corerct for weighted sum. + // 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]; - e.counts[i] += ncall; + 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; } - e.ncall += ncall; + } + { + // 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 = { + /* .no_alloc = */ false, // the data is needed + /* .ctx = */ &ctx, + }; + struct gguf_context * ctx_gguf = gguf_init_from_file(file_name, meta_gguf_params); + if (!ctx_gguf) { + return this->load_imatrix_legacy(file_name); + } + const int32_t n_entries = gguf_get_n_tensors(ctx_gguf); + if (n_entries < 1) { + LOG_ERR("%s: no data in file %s\n", __func__, file_name); + gguf_free(ctx_gguf); + ggml_free(ctx); + return false; + } + + 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 + // and also makes it easier to re-use a similar implementation in quantize.cpp + // Using an ordered map to get a deterministic iteration order. + std::map> sums_counts_for; + + for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { + std::string name = cur->name; + + if (name.empty()) { continue; } + + 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[std::move(name)].second = cur; + } else { + // ignore other tensors + } + } + + for (const auto & sc : sums_counts_for) { + const std::string & name = sc.first; + const struct ggml_tensor * in_sum2 = sc.second.first; + const struct ggml_tensor * counts = sc.second.second; + + 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); + return false; + } + + auto & e = m_stats[name]; + + int64_t nval = ggml_nelements(in_sum2); + if (e.values.empty()) { + 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); + ggml_free(ctx); + return false; + } + + int64_t ncounts = ggml_nelements(counts); + if (e.counts.empty()) { + e.counts.resize(ncounts, 0); + } else if (e.counts.size() == 1 && ncounts > 1) { + // broadcast, when loading an old imatrix + e.counts.resize(ncounts, e.counts[0]); + } else if ((size_t) ncounts != e.counts.size()) { + LOG_ERR("%s: mismatched counts size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) ncounts, e.counts.size()); + gguf_free(ctx_gguf); + ggml_free(ctx); + return false; + } + + // Recreate the state as expected by save_imatrix() + for (int64_t j = 0; j < nval; 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; } @@ -425,12 +718,11 @@ static void process_logits( } } -static bool compute_imatrix(llama_context * ctx, const common_params & params) { +static bool compute_imatrix(llama_context * ctx, const common_params & params, const int32_t n_ctx) { const llama_model * model = llama_get_model(ctx); const llama_vocab * vocab = llama_model_get_vocab(model); const bool add_bos = llama_vocab_get_add_bos(vocab); - const int n_ctx = llama_n_ctx(ctx); GGML_ASSERT(!llama_vocab_get_add_eos(vocab)); @@ -475,45 +767,61 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) { double nll = 0.0; double nll2 = 0.0; - LOG_INF("%s: computing over %d chunks with batch_size %d\n", __func__, n_chunk, n_batch); + const int num_batches = (n_ctx + n_batch - 1) / n_batch; + const int n_seq = std::max(1, n_batch / n_ctx); - std::vector workers(std::thread::hardware_concurrency() - 1); + GGML_ASSERT(n_batch < n_ctx || n_batch % n_ctx == 0); + GGML_ASSERT(params.n_ctx == n_seq * n_ctx); - const int num_batches = (n_ctx + n_batch - 1) / n_batch; + llama_batch batch = llama_batch_init(std::min(n_batch, n_ctx*n_seq), 0, 1); std::vector logits; if (params.compute_ppl && num_batches > 1) { logits.reserve((size_t)n_ctx * n_vocab); } - for (int i = 0; i < n_chunk; ++i) { + LOG_INF("%s: computing over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq); + + std::vector workers(std::thread::hardware_concurrency() - 1); + + for (int i = 0; i < n_chunk; i += n_seq) { const int start = i * n_ctx; const int end = start + n_ctx; - std::vector logits; + const int n_seq_batch = std::min(n_seq, n_chunk - i); const auto t_start = std::chrono::high_resolution_clock::now(); // clear the KV cache llama_kv_self_clear(ctx); - llama_batch batch = llama_batch_init(n_batch, 0, 1); - for (int j = 0; j < num_batches; ++j) { const int batch_start = start + j * n_batch; const int batch_size = std::min(end - batch_start, n_batch); - // save original token and restore it after eval - const auto token_org = tokens[batch_start]; + // clear the batch + common_batch_clear(batch); - // add BOS token for the first batch of each chunk - if (add_bos && j == 0) { - tokens[batch_start] = llama_vocab_bos(vocab); - } + for (int seq = 0; seq < n_seq_batch; seq++) { + int seq_start = batch_start + seq*n_ctx; - common_batch_clear(batch); - for (int i = 0; i < batch_size; i++) { - common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true); + // save original token and restore it after eval + const auto token_org = tokens[seq_start]; + + // add BOS token for the first batch of each chunk + if (add_bos && j == 0) { + tokens[seq_start] = llama_vocab_bos(vocab); + } + for (int k = 0; k < batch_size; ++k) { + // NOTE: specifying all logits to get activations for the output.weight tensor + // and also for the perplexity calculation. + // TODO: only get outputs when (params.process_output || params.compute_ppl) + // (not possible when this skips FFN computation of the last layer) + common_batch_add(batch, tokens[seq_start + k], j*n_batch + k, { seq }, true); + } + + // restore the original token in case it was set to BOS + tokens[seq_start] = token_org; } if (llama_decode(ctx, batch)) { @@ -522,23 +830,19 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) { return false; } - // restore the original token in case it was set to BOS - tokens[batch_start] = token_org; - if (params.compute_ppl && num_batches > 1) { const auto * batch_logits = llama_get_logits(ctx); logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab); } } - llama_batch_free(batch); - - const auto t_end = std::chrono::high_resolution_clock::now(); if (i == 0) { + llama_synchronize(ctx); + const auto t_end = std::chrono::high_resolution_clock::now(); const float t_total = std::chrono::duration(t_end - t_start).count(); LOG_INF("%s: %.2f seconds per pass - ETA ", __func__, t_total); - int total_seconds = (int)(t_total * n_chunk); + int total_seconds = (int)(t_total * n_chunk / n_seq); if (total_seconds >= 60*60) { LOG("%d hours ", total_seconds / (60*60)); total_seconds = total_seconds % (60*60); @@ -548,17 +852,27 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) { if (params.compute_ppl) { const int first = n_ctx/2; - const auto * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx); - process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first, - workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first); - count += n_ctx - first - 1; + for (int seq = 0; seq < n_seq_batch; seq++) { + const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx); + + llama_token * tokens_data = tokens.data() + start + seq*n_ctx + first; + + process_logits(n_vocab, all_logits + first*n_vocab, + tokens_data, n_ctx - 1 - first, + workers, nll, nll2, + logit_history.data() + start + seq*n_ctx + first, + prob_history.data() + start + seq*n_ctx + first); - LOG("[%d]%.4lf,", i + 1, std::exp(nll / count)); + count += n_ctx - first - 1; + + LOG("[%d]%.4lf,", i + seq + 1, std::exp(nll / count)); + } fflush(stdout); logits.clear(); } } + LOG("\n"); if (params.compute_ppl) { @@ -574,13 +888,15 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) { } } + llama_batch_free(batch); + return true; } 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; @@ -592,7 +908,22 @@ int main(int argc, char ** argv) { common_init(); - params.n_batch = std::min(params.n_batch, params.n_ctx); + const int32_t n_ctx = params.n_ctx; + + if (n_ctx <= 0) { + LOG_ERR("%s: imatrix tool requires '--ctx-size' > 0\n", __func__); + return 1; + } + + { + const int32_t n_seq = std::max(1, params.n_batch / n_ctx); + const int32_t n_kv = n_seq * n_ctx; + + params.n_parallel = n_seq; + params.n_ctx = n_kv; + + params.n_batch = std::min(params.n_batch, n_kv); + } g_collector.set_params(params); @@ -648,7 +979,7 @@ int main(int argc, char ** argv) { } LOG_INF("No prompt provided; combining precomputed matrices only.\n"); } else { - if (!compute_imatrix(ctx, params)) { + if (!compute_imatrix(ctx, params, n_ctx)) { return 1; } } diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index a4468b1698722..1a37cf316f4de 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -1,11 +1,13 @@ #include "common.h" #include "llama.h" +#include "gguf.h" #include #include #include #include #include +#include #include #include #include @@ -61,6 +63,11 @@ static const char * const LLM_KV_QUANTIZE_IMATRIX_DATASET = "quantize.imatrix static const char * const LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES = "quantize.imatrix.entries_count"; 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_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"; + static bool striequals(const char * a, const char * b) { while (*a && *b) { if (std::tolower(*a) != std::tolower(*b)) { @@ -77,7 +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; @@ -86,7 +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; @@ -119,7 +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 { @@ -130,7 +137,16 @@ static void usage(const char * executable) { exit(1); } -static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_dataset, std::unordered_map> & imatrix_data) { +// TODO: share with implementation in imatrix.cpp +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; + if (has_suffix) { + str = str.substr(0, str.size() - suffix.size()); + } + return has_suffix; +} + +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()); @@ -186,15 +202,130 @@ static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_ in.read((char *)&dataset_len, sizeof(dataset_len)); std::vector dataset_as_vec(dataset_len); in.read(dataset_as_vec.data(), dataset_len); - imatrix_dataset.assign(dataset_as_vec.begin(), dataset_as_vec.end()); - printf("%s: imatrix dataset='%s'\n", __func__, imatrix_dataset.c_str()); + 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 = { + /* .no_alloc = */ false, // the data is needed + /* .ctx = */ &ctx, + }; + struct gguf_context * ctx_gguf = gguf_init_from_file(imatrix_file.c_str(), meta_gguf_params); + if (!ctx_gguf) { + 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) { + fprintf(stderr, "%s: no data in file %s\n", __func__, imatrix_file.c_str()); + gguf_free(ctx_gguf); + ggml_free(ctx); + exit(1); + } + + 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) { + fprintf(stderr, "%s: missing imatrix metadata in file %s\n", __func__, imatrix_file.c_str()); + gguf_free(ctx_gguf); + ggml_free(ctx); + exit(1); + } + + const uint32_t chunk_size = gguf_get_val_u32(ctx_gguf, chunk_size_idx); + + 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; + + for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) { + std::string name = cur->name; + + if (name.empty()) { continue; } + + if (str_remove_suffix(name, sums_suffix)) { + // in_sum2 + sums_counts_for[std::move(name)].first = cur; + } else if (str_remove_suffix(name, counts_suffix)) { + // counts + sums_counts_for[std::move(name)].second = cur; + } else { + // 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; + + if (!sums || !counts) { + fprintf(stderr, "%s: mismatched sums and counts for %s\n", __func__, name.c_str()); + gguf_free(ctx_gguf); + ggml_free(ctx); + exit(1); + } + + const int64_t ne0 = sums->ne[0]; + const int64_t ne1 = sums->ne[1]; + + auto & e = imatrix_data[name]; + e.resize(ggml_nelements(sums)); + float max_count = 0.0f; + for (int64_t j = 0; j < ne1; ++j) { + const float count = ((const float *) counts->data)[j]; + 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; + } + } + if (getenv("LLAMA_TRACE")) { + printf("%s: loaded data (size = %6d, n_tokens = %6d, n_chunks = %6d) for '%s'\n", __func__, int(e.size()), int(max_count), int(max_count / chunk_size), name.c_str()); + } + } + + int m_last_chunk = gguf_get_val_u32(ctx_gguf, chunk_count_idx); + + 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); + ggml_free(ctx); + + return m_last_chunk; +} + 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) { @@ -206,18 +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)); @@ -318,9 +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; { @@ -331,11 +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)); } diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 162070e6e193a..c2dbf7b643dc5 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -216,6 +216,12 @@ class Adapter: TYPE = "adapter.type" LORA_ALPHA = "adapter.lora.alpha" + class IMatrix: + CHUNK_COUNT = "imatrix.chunk_count" + CHUNK_SIZE = "imatrix.chunk_size" + DATASETS = "imatrix.datasets" + + # # recommended mapping of model tensor names for storage in gguf # @@ -224,6 +230,7 @@ class Adapter: class GGUFType: MODEL = "model" ADAPTER = "adapter" + IMATRIX = "imatrix" class MODEL_ARCH(IntEnum):