diff --git a/common/common.h b/common/common.h index a8cb630ea5805..f6641342b5372 100644 --- a/common/common.h +++ b/common/common.h @@ -481,10 +481,11 @@ struct common_params { int32_t i_chunk = 0; // start processing from this chunk int8_t imat_dat = 0; // whether the legacy imatrix.dat format should be output (gguf <= 0 < dat) - bool process_output = false; // collect data for the output tensor - bool compute_ppl = true; // whether to compute perplexity - bool show_statistics = false; // show imatrix statistics per tensor - bool parse_special = false; // whether to parse special tokens during imatrix tokenization + bool process_output = false; // collect data for the output tensor + bool compute_ppl = true; // whether to compute perplexity + bool show_statistics = false; // show imatrix statistics per tensor + bool activation_statistics = false; // generate data to calculate activation based statistics + bool parse_special = false; // whether to parse special tokens during imatrix tokenization // cvector-generator params int n_pca_batch = 100; diff --git a/tools/imatrix/README.md b/tools/imatrix/README.md index 4505cb4ce8c7d..e823d25c19ae1 100644 --- a/tools/imatrix/README.md +++ b/tools/imatrix/README.md @@ -10,7 +10,7 @@ More information is available in =2`, a message is output each time data is collected for any tensor. Default verbosity level is `1`. * `-o | --output-file` specifies the name of the file where the computed data will be stored. If missing `imatrix.gguf` is used. * `-ofreq | --output-frequency` specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks) -* `--output-format` specifies the output format of the generated imatrix file. Either "gguf", or "dat" (the legacy format). Defaults to "gguf". +* `--output-format` specifies the output format of the generated imatrix file. Either `gguf`, or `dat` (the legacy format). Defaults to `gguf`. * `--save-frequency` specifies how often to save a copy of the imatrix in a separate file. Default is 0 (i.e., never) * `--process-output` specifies if data will be collected for the `output.weight` tensor. Typically, it is better not to utilize the importance matrix when quantizing `output.weight`, so this is set to `false` by default. * `--in-file` one or more existing imatrix files to load and combine. Useful for merging files from multiple runs/datasets. * `--parse-special` enables parsing of special tokens (e.g., `<|im_start|>` in some models). Useful for models with custom tokenizers. * `--chunk | --from-chunk` to skip the first `n` chunks of tokens from the input data. Useful for resuming or skipping initial low-quality data. -* `--chunks` maximum number of chunks to process. Default is -1 for all available chunks. +* `--chunks` maximum number of chunks to process. Default is `-1` for all available chunks. * `--no-ppl` disables the calculation of perplexity for the processed chunks. Useful if you want to speed up the processing and do not care about perplexity. * `--show-statistics` displays imatrix file's statistics. For faster computation, make sure to use GPU offloading via the `-ngl | --n-gpu-layers` argument. -Recent versions of `llama-imatrix` store data in GGUF format by default. For the legacy format, use an extension other than `.gguf` when saving the output file. More information is available in . +Versions **b5942** and newer of `llama-imatrix` store data in GGUF format by default. For the legacy format, use `--output-format dat` when saving the output file. More information is available in . ## Examples @@ -74,25 +74,25 @@ Recent versions of `llama-imatrix` store data in GGUF format by default. For the ./llama-imatrix --in-file imatrix.gguf --show-statistics ``` -`--show-statistics` will display the following statistics: +## Statistics #### Per tensor -* Σ(Act²): sum of all squared activations (the importance scores) -* Min & Max: minimum and maximum squared activations values -* μ & σ: Squared activations' mean and standard deviation -* % Active: proportion of elements whose average squared activation exceeds a small threshold (1e-5). Helpful to determine how alive/dormant the tensor is during inference -* N: number of squared activations -* Entropy: entropy of the squared activation distribution, in bits (standard Shannon entropy measurement) $S = -\sum_{i=1}^N p_i \log_2 p_i$ -* E (norm): Normalized entropy. $E(norm)=\frac{-\sum_{i=1}^N p_i \log_2 p_i}{log_2 N}$. These two metrics can be used to determine how well a prompt "exercises" the model's capabilities -* ZD Score: z-score distribution as described in _3.1 Layer Importance Scores_ of [Layer-Wise Quantization](https://arxiv.org/abs/2406.17415) -* CosSim: cosine similarity with respect to the previous layer's tensor. Useful to determine how similar the squared activations of the current layer are to the previous layer's squared activations. +* **Σ(Act²)** *(legacy mode)* / **L₂ Norm** *(preferred)*: If in legacy mode, the raw sum of squares of activations (sum of `Act²`). In preferred mode, the Euclidean Distance (L₂ Norm) between this tensor’s average activations and those of the previous layer. +* **Min / Max / μ / σ**: Tensor elements Min, Max, Mean, and Standard Deviation. +* **N**: Number of tensor elements considered. +* **H Norm**: Shannon Entropy normalized over log₂(N). Defined as $H Norm=\frac{-\sum_{i=1}^N p_i \log_2 p_i}{log_2 N}$. Used to determine how well a prompt "exercises" the model's capabilities. +* **H** *(legacy mode)* / **ECS** *(preferred)*: If legacy, Shannon Entropy defined as $H = -\sum_{i=1}^N p_i \log_2 p_i$. If preferred, *Euclidean-Cosine Score* defined as $ECS = K \cdot e^{-\alpha a} \cdot |b|^{\gamma}$ where `a = L₂ Norm`, `b = Cosine Similarity`, `α = 0.01`, `γ = 10` between this tensor’s elements and those of the previous layer. Higher score means more similarity and lower change. +* **ZD**: % of elements whose Z-score is > 1.0 in magnitude (an indicator of outliers), as described in _3.1 Layer Importance Scores_ of [Layer-Wise Quantization](https://arxiv.org/abs/2406.17415) +* **CosSim**: Cosine Similarity of the mean activations between this tensor’s elements and those of the previous layer. #### Per layer -Weighted averages of Σ(Act²), ZD Score and CosSim are also calculated. +Aggregated metrics per block/layer: -#### Important note on the computed Statistics +* **Σ(Act²)** *(legacy mode)* / **L₂ Norm** *(preferred)*: If in legacy mode, the sum of squared activations (sum of Act²) for the layer's concatenated tensors. In preferred mode, the Euclidean Distance (L₂ Norm) between this layer's average concatenated tensor activations the previous layer. +* **ZD**: % of this layer's concatenated tensors' elements with |Z| > 1. +* **CosSim**: Cosine Similarity of the mean activations between this layer's concatenated tensors' elements compared and the previous layer’s. +* **ECS** *(preferred only)*: Euclidean-Cosine Score applied to the layer. -When using these statistics, please note that they are computed on the squared activations, **not on the actual (raw) activations**. -Whilst the results are still useful, they're less realiable than using the raw values, and in the case of the cosine similarity, could be misleading if the tensor contains opposite vectors. +More information is available in https://github.com/ggml-org/llama.cpp/pull/14891 diff --git a/tools/imatrix/imatrix.cpp b/tools/imatrix/imatrix.cpp index f28a036deebe3..1c6f59c782f70 100644 --- a/tools/imatrix/imatrix.cpp +++ b/tools/imatrix/imatrix.cpp @@ -1,8 +1,8 @@ #include "arg.h" #include "common.h" -#include "log.h" -#include "llama.h" #include "gguf.h" +#include "llama.h" +#include "log.h" #include #include @@ -10,14 +10,15 @@ #include #include #include -#include -#include -#include #include -#include #include -#include +#include #include +#include +#include +#include +#include +#include #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data @@ -29,7 +30,7 @@ static void print_usage(int, char ** argv) { " -m model.gguf -f some-text.txt [-o imatrix.gguf] [--output-format {gguf,dat}] [--no-ppl] \\\n" " [--process-output] [--chunk 123] [--save-frequency 0] [--output-frequency 10] \\\n" " [--in-file imatrix-prev-0.gguf --in-file imatrix-prev-1.gguf ...] [--parse-special] \\\n" - " [--show-statistics] [...]\n" , argv[0]); + " [--output-format gguf|dat] [--show-statistics] [...]\n" , argv[0]); LOG("\n"); } @@ -38,6 +39,7 @@ 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 activations; std::vector values; std::vector counts; }; @@ -45,16 +47,16 @@ struct Stats { struct tensor_statistics { std::string tensor; Stats stats; - float total_sqract = 0.0f; - float mean_sqract = 0.0f; - float max_sqract = 0.0f; - float min_sqract = 0.0f; - int elements = 0; - float stddev = 0.0f; - float active = 0.0f; - float entropy = 0.0f; - float zd = 0.0f; - float cossim = 0.0f; + float sum_values = 0.0f; + float mean_values = 0.0f; + float max_values = 0.0f; + float min_values = 0.0f; + int elements = 0; + float std_deviation = 0.0f; + float entropy = 0.0f; + float zd_score = 0.0f; + float cossim = 0.0f; + float l2_dist = 0.0f; }; class IMatrixCollector { @@ -125,97 +127,278 @@ static void process_tensor_name(const std::string & input, std::string & layer, } } -static void compute_statistics(std::vector & tstats, const std::string & name, const Stats & e) { - if (e.values.size() % e.counts.size() != 0) { - LOG_ERR("%s: activation size mismatch for tensor %s (%zu vs %zu)\n", __func__, name.c_str(), e.counts.size(), e.values.size()); - return; - } - if (e.counts.empty()) { - LOG_ERR("%s: there are no activations for tensor %s. The imatrix may be suboptimal\n", __func__, name.c_str()); - return; +static std::vector compute_tensor_averages(const Stats & tstats) { + if (tstats.counts.empty()) { return {}; } + const size_t n_mat = tstats.counts.size(); + const size_t len = !tstats.activations.empty() ? tstats.activations.size() : tstats.values.size(); + if (len == 0 || n_mat == 0 || len % n_mat != 0) { return {}; } + const size_t row = len / n_mat; + std::vector vec; + vec.reserve(len); + + if (tstats.activations.empty()) { + // Mean of squares + for (size_t m = 0; m < n_mat; ++m) { + const auto c = (float)tstats.counts[m]; + const size_t off = m * row; + if (c <= 0.0f) { + vec.insert(vec.end(), row, 0.0f); // zero-fill rows for experts with zero count to preserve shape + continue; + } + for (size_t j = 0; j < row; ++j) { vec.push_back(tstats.values[off + j] / c); } + } + } else { + // Mean + for (size_t m = 0; m < n_mat; ++m) { + const auto c = (float)tstats.counts[m]; + const size_t off = m * row; + if (c <= 0.0f) { + vec.insert(vec.end(), row, 0.0f); // zero-fill rows for experts with zero count to preserve shape + continue; + } + for (size_t j = 0; j < row; ++j) { vec.push_back(tstats.activations[off + j] / c); } + } } - const int n_mat = e.counts.size(); - const int row_size = e.values.size() / n_mat; + return vec; +} - std::vector activations; - activations.reserve(e.values.size()); +static bool compute_vector_statistics(std::vector & tstats, const std::string & name, const Stats & e) { + const size_t n_mat = e.counts.size(); + const size_t len = e.activations.empty() ? e.values.size() : e.activations.size(); + const bool legacy = e.activations.empty(); + if (n_mat == 0) { + LOG_ERR("%s: there are no activations for tensor %s. The imatrix may be suboptimal\n", __func__, name.c_str()); + return false; + } + if (len == 0 || (len % n_mat) != 0) { + LOG_ERR("%s: activation size mismatch for tensor %s (len=%zu, counts=%zu)\n", __func__, name.c_str(), len, n_mat); + return false; + } + if (!legacy && e.values.size() != len) { + LOG_ERR("%s: activations/values size mismatch for tensor %s (act=%zu, val=%zu)\n", __func__, name.c_str(), len, e.values.size()); + return false; + } - for (int i = 0; i < n_mat; ++i) { - for (int j = 0; j < row_size; ++j) { - activations.push_back(e.values[i*row_size + j] / e.counts[i]); + const size_t row_size = len / n_mat; + double mean = 0.0; + double M2 = 0.0; + double sum = 0.0; + float vmin = std::numeric_limits::infinity(); + float vmax = -std::numeric_limits::infinity(); + double energy_sum = 0.0; + size_t valid_n = 0; + for (size_t i = 0; i < n_mat; ++i) { + const auto c = (float)e.counts[i]; + if (c <= 0.0f) { continue; } // skip experts with zero count + const size_t off = i * row_size; + + for (size_t j = 0; j < row_size; ++j) { + const double v_avg = legacy ? 0.0 : (double)e.activations[off + j] / (double)c; // E[x] + const double v_energy = (double)e.values[off + j] / (double)c; // E[x^2] + const double v = legacy ? v_energy : v_avg; + + ++valid_n; + sum += v; + vmin = std::min(vmin, (float)v); + vmax = std::max(vmax, (float)v); + + const double delta = v - mean; + mean += delta / (double)valid_n; + M2 += delta * (v - mean); + energy_sum += std::max(0.0, v_energy); } } - const float act_total = std::accumulate(activations.begin(), activations.end(), 0.0f); - const float act_max = *std::max_element(activations.begin(), activations.end()); - const float act_min = *std::min_element(activations.begin(), activations.end()); - const float act_mean = act_total / activations.size(); - const float act_sqr_total = std::inner_product(activations.begin(), activations.end(), activations.begin(), 0.0f); - const float act_var = (act_sqr_total / activations.size()) - (act_mean * act_mean); - const float act_dev = std::sqrt(std::max(0.0f, act_var)); - float threshold = 1e-5f; - const int inactive_count = std::count_if(activations.begin(), activations.end(), - [threshold](const float v) { return fabsf(v) <= threshold; }); - const float active_ratio = 1 - static_cast(inactive_count) / activations.size(); + if (valid_n == 0) { + LOG_ERR("%s: there are no activations for tensor %s. The imatrix may be suboptimal\n", __func__, name.c_str()); + return false; + } - float entropy = 0; - if (act_total > 0) { - for (const auto act : activations) { - if (const float p = act / act_total; p > 0) { - entropy -= p * std::log2(p); + float std_deviation = 0.0f; + float entropy = 0.0f; + double zd_count = 0.0; + double variance = valid_n > 1 ? M2 / ((double)valid_n - 1) : 0.0; + variance = std::max(variance, 0.0); + std_deviation = std::sqrt((float)variance); + if (energy_sum > 0.0) { + for (size_t i = 0; i < n_mat; ++i) { + const auto c = (float)e.counts[i]; + if (c <= 0.0f) { continue; } + const size_t off = i * row_size; + for (size_t j = 0; j < row_size; ++j) { + const double v_energy = (double)e.values[off + j] / (double)c; // E[x^2] + const double w = std::max(0.0, v_energy); + const double p = w / energy_sum; + if (p > 0.0) { entropy -= (float)(p * std::log2(p)); } } } } - - int z_score = 0; - if (act_dev > 0.0f) { - for (const auto act : activations) { - if (const float p = (act - act_mean) / act_dev; p > 1) { - z_score++; + if (std_deviation > 0.0f) { + for (size_t i = 0; i < n_mat; ++i) { + const float c = (float)e.counts[i]; + if (c <= 0.0f) { continue; } + const size_t off = i * row_size; + for (size_t j = 0; j < row_size; ++j) { + const double v_avg = legacy ? 0.0 : (double)e.activations[off + j] / (double)c; // E[x] + const double v_energy = (double)e.values[off + j] / (double)c; // E[x^2] + const float v = (float)(legacy ? v_energy : v_avg); + const float z = (v - (float)mean) / std_deviation; + if (std::fabs(z) > 1.0f) { zd_count += 1.0; } } } } auto & ts = tstats.emplace_back(); - ts.tensor = name; - ts.stats = e; - ts.total_sqract = act_total; - ts.mean_sqract = act_mean; - ts.max_sqract = act_max; - ts.min_sqract = act_min; - ts.elements = static_cast(activations.size()); - ts.stddev = act_dev; - ts.active = active_ratio; - ts.entropy = entropy; - ts.zd = static_cast(z_score) / ts.elements; + ts.tensor = name; + ts.stats = e; + ts.sum_values = (float)sum; + ts.mean_values = (float)mean; + ts.max_values = vmax; + ts.min_values = vmin; + ts.elements = valid_n; + ts.std_deviation = std_deviation; + ts.entropy = entropy; + ts.zd_score = (float)(zd_count / (double)valid_n); + + return e.activations.empty(); } -static void compute_cossim(std::vector & tstats) { +static void compute_tensor_statistics(std::vector & tstats) { static const std::regex pattern(R"(blk\.(\d+)\.)"); for (auto & ts : tstats) { + ts.cossim = 0.0f; + ts.l2_dist = 0.0f; + if (std::smatch match; std::regex_search(ts.tensor, match, pattern)) { const int blk = std::stoi(match[1]); + if (blk <= 0) { continue; } std::string tname(ts.tensor); - tname.replace(match.position(1), match.length(1), std::to_string(blk-1)); - auto prev = std::find_if(tstats.begin(), tstats.end(), + tname.replace(match.position(1), match.length(1), std::to_string(blk - 1)); + auto prev_it = std::find_if(tstats.begin(), tstats.end(), [tname](const tensor_statistics & t) { return t.tensor == tname; }); - if (prev != tstats.end()) { - const float dp = std::inner_product(ts.stats.values.begin(), ts.stats.values.end(), - prev->stats.values.begin(), 0.0f); - const float curr_mag = std::sqrt(std::inner_product(ts.stats.values.begin(), ts.stats.values.end(), - ts.stats.values.begin(), 0.0f)); - const float prev_mag = std::sqrt(std::inner_product(prev->stats.values.begin(), prev->stats.values.end(), - prev->stats.values.begin(), 0.0f)); - const float cs = dp / (curr_mag * prev_mag); - ts.cossim = cs; + if (prev_it == tstats.end()) { continue; } + + const auto curr_avg = compute_tensor_averages(ts.stats); + const auto prev_avg = compute_tensor_averages(prev_it->stats); + if (curr_avg.empty() || curr_avg.size() != prev_avg.size()) { continue; } + + float dot_prod = 0.0f; + float norm1_sq = 0.0f; + float norm2_sq = 0.0f; + float l2_dist_sq = 0.0f; + + for (size_t i = 0; i < curr_avg.size(); ++i) { + const float c_val = curr_avg[i]; + const float p_val = prev_avg[i]; + dot_prod += c_val * p_val; + norm1_sq += c_val * c_val; + norm2_sq += p_val * p_val; + const float diff = c_val - p_val; + l2_dist_sq += diff * diff; } - } else { - ts.cossim = 0; + + // Compute Cosine Similarity + float cs = 0.0f; + if (norm1_sq > 0.0f && norm2_sq > 0.0f) { + cs = dot_prod / (std::sqrt(norm1_sq) * std::sqrt(norm2_sq)); + cs = std::min(cs, 1.0f); + cs = std::max(cs, -1.0f); + } else if (norm1_sq == 0.0f && norm2_sq == 0.0f) { + cs = 1.0f; + } + ts.cossim = cs; + + // Compute L2 Norm (Euclidean Distance) + ts.l2_dist = std::sqrt(l2_dist_sq); } } } +static void compute_layer_statistics(const std::vector & tstats, + std::map & layer_cossim, + std::map & layer_l2_dist, + const std::unordered_map & stats_map) { + struct layer_aggregation { + double sum_dot_prod = 0.0; + double sum_norm1_sq = 0.0; + double sum_norm2_sq = 0.0; + double sum_l2_dist_sq = 0.0; + int n_tensors = 0; + }; + + static const std::regex pattern(R"(blk\.(\d+)\.)"); + std::unordered_map tidx; + tidx.reserve(tstats.size()); + for (const auto & ts : tstats) { tidx[ts.tensor] = &ts; } + + std::map agr; + for (const auto & ts : tstats) { + std::smatch match; + if (!std::regex_search(ts.tensor, match, pattern)) { continue; } + const int blk = std::stoi(match[1]); + if (blk <= 0) { continue; } + + std::string prev_lyr(ts.tensor); + prev_lyr.replace(match.position(1), match.length(1), std::to_string(blk - 1)); + if (tidx.find(prev_lyr) == tidx.end()) { continue; } + + auto it_curr = stats_map.find(ts.tensor); + auto it_prev = stats_map.find(prev_lyr); + if (it_curr == stats_map.end() || it_prev == stats_map.end()) { continue; } + + const auto curr_avg = compute_tensor_averages(it_curr->second); + const auto prev_avg = compute_tensor_averages(it_prev->second); + if (curr_avg.empty() || prev_avg.empty() || curr_avg.size() != prev_avg.size()) { continue; } + + // Compute statistics for each tensor pair individually + double dot_prod = 0.0; + double norm1_sq = 0.0; + double norm2_sq = 0.0; + double l2_dist_sq = 0.0; + + for (size_t i = 0; i < curr_avg.size(); ++i) { + const double c_val = curr_avg[i]; + const double p_val = prev_avg[i]; + dot_prod += c_val * p_val; + norm1_sq += c_val * c_val; + norm2_sq += p_val * p_val; + const double diff = c_val - p_val; + l2_dist_sq += diff * diff; + } + + if (norm1_sq == 0.0 && norm2_sq == 0.0) { continue; } + + // Accumulate statistics for the layer + auto & entry = agr[blk]; + entry.sum_dot_prod += dot_prod; + entry.sum_norm1_sq += norm1_sq; + entry.sum_norm2_sq += norm2_sq; + entry.sum_l2_dist_sq += l2_dist_sq; + entry.n_tensors++; + } + + // Compute aggregated layer statistics + for (auto & kv : agr) { + const auto & agg = kv.second; + if (agg.n_tensors == 0) { continue; } + + // Compute aggregated Cosine Similarity + float cossim = 0.0f; + if (agg.sum_norm1_sq > 0.0 && agg.sum_norm2_sq > 0.0) { + cossim = agg.sum_dot_prod / (std::sqrt(agg.sum_norm1_sq) * std::sqrt(agg.sum_norm2_sq)); + cossim = std::min(cossim, 1.0f); + cossim = std::max(cossim, -1.0f); + } else if (agg.sum_norm1_sq == 0.0 && agg.sum_norm2_sq == 0.0) { + cossim = 1.0f; + } + layer_cossim[kv.first] = cossim; + + // Compute aggregated L2 Distance (Euclidean Distance) + layer_l2_dist[kv.first] = (float)std::sqrt(agg.sum_l2_dist_sq); + } +} + bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) { GGML_UNUSED(user_data); @@ -281,6 +464,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * e.counts.resize(n_as, e.counts[0]); } if (e.values.empty()) { + e.activations.resize(src1->ne[0]*n_as, 0); e.values.resize(src1->ne[0]*n_as, 0); e.counts.resize(n_as, 0); } @@ -312,6 +496,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * e.counts[ex]++; for (int64_t j = 0; j < src1->ne[0]; ++j) { + e.activations[e_start + j] += x[j]; e.values[e_start + j] += x[j] * x[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()); @@ -351,6 +536,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * } } if (e.values.empty()) { + e.activations.resize(src1->ne[0] * n_mat, 0); e.values.resize(src1->ne[0] * n_mat, 0); e.counts.resize(1, 0); } @@ -369,6 +555,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * for (int64_t row = 0; row < src1->ne[1]; ++row) { const float * x = (const float *) (data + row * src1->nb[1] + i2 * src1->nb[2] + i3 * src1->nb[3]); for (int64_t j = 0; j < src1->ne[0]; ++j) { + e.activations[mat_start + j] += x[j]; e.values[mat_start + j] += x[j] * x[j]; if (!std::isfinite((float)e.values[j])) { LOG_ERR("%f detected in %s\n", (float)e.values[j], wname.c_str()); @@ -550,13 +737,30 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const { } to_store.push_back(kv.first); + data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.activations.size(), GGML_MEM_ALIGN); 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); + data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * 4, GGML_MEM_ALIGN); } // deterministic tensor name order std::sort(to_store.begin(), to_store.end()); + // Compute per-tensor statistics (CosSim, L2 Dist, ECS) to store alongside sums + std::vector tstats; + tstats.reserve(m_stats.size()); + bool legacy_mode = true; + for (const auto & kv : m_stats) { + const bool is_legacy = compute_vector_statistics(tstats, kv.first, kv.second); + legacy_mode = legacy_mode && is_legacy; + } + if (!tstats.empty()) { compute_tensor_statistics(tstats); } + + // index by tensor name + std::unordered_map tstat_index; + tstat_index.reserve(tstats.size()); + for (const auto & ts : tstats) { tstat_index[ts.tensor] = &ts; } + struct ggml_init_params params = { /* .mem_size = */ data_size, /* .mem_buffer = */ NULL, @@ -602,6 +806,39 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const { gguf_add_tensor(ctx_gguf, in_sum2); gguf_add_tensor(ctx_gguf, counts); + + if (!stat.activations.empty()) { + const int32_t nact = (int32_t) stat.activations.size(); + struct ggml_tensor * in_sum = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nact / nmat, nmat); + ggml_format_name(in_sum, "%s.in_sum", name.c_str()); + for (int32_t j = 0; j < nact; ++j) { + ((float *) in_sum->data)[j] = (float) stat.activations[j]; + } + gguf_add_tensor(ctx_gguf, in_sum); + } + } + + // Store per-tensor statistics as a small 1D tensor: [ECS, L2 Dist, CosSim, ZD Score] + { + float l2 = 0.0f; + float cs = 0.0f; + float zd = 0.0f; + float ecs = 0.0f; + auto it_ts = tstat_index.find(name); + if (it_ts != tstat_index.end() && it_ts->second != nullptr) { + l2 = it_ts->second->l2_dist; + cs = it_ts->second->cossim; + zd = it_ts->second->zd_score; + ecs = 100.0f * (1.0f - std::exp(-0.01f * l2) * std::pow(std::fabs(cs), 10.0f)); + } + + struct ggml_tensor * stats_t = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4); + ggml_format_name(stats_t, "%s.stats", name.c_str()); + ((float *)stats_t->data)[0] = ecs; + ((float *)stats_t->data)[1] = l2; + ((float *)stats_t->data)[2] = cs; + ((float *)stats_t->data)[3] = zd; + gguf_add_tensor(ctx_gguf, stats_t); } } @@ -740,6 +977,7 @@ bool IMatrixCollector::load_imatrix(const char * file_name) { } } + const std::string in_sum_suffix{ ".in_sum" }; const std::string in_sum2_suffix{ ".in_sum2" }; const std::string counts_suffix{ ".counts" }; @@ -747,7 +985,7 @@ bool IMatrixCollector::load_imatrix(const char * file_name) { // 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; + 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; @@ -756,21 +994,26 @@ bool IMatrixCollector::load_imatrix(const char * file_name) { if (string_remove_suffix(name, in_sum2_suffix)) { // in_sum2 - sums_counts_for[std::move(name)].first = cur; + std::get<0>(sums_counts_for[std::move(name)]) = cur; } else if (string_remove_suffix(name, counts_suffix)) { // counts - sums_counts_for[std::move(name)].second = cur; - } else { + std::get<1>(sums_counts_for[std::move(name)]) = cur; + } else if (string_remove_suffix(name, in_sum_suffix)) { + // in_sum + std::get<2>(sums_counts_for[std::move(name)]) = 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; + const struct ggml_tensor * in_sum = std::get<2>(sc.second); + const struct ggml_tensor * in_sum2 = std::get<0>(sc.second); + const struct ggml_tensor * counts = std::get<1>(sc.second); - if (!in_sum2 || !counts) { + if (!in_sum2 || !counts || (in_sum != nullptr && ggml_nelements(in_sum) != ggml_nelements(in_sum2))) { LOG_ERR("%s: mismatched sums and counts for %s\n", __func__, name.c_str()); gguf_free(ctx_gguf); ggml_free(ctx); @@ -782,6 +1025,9 @@ bool IMatrixCollector::load_imatrix(const char * file_name) { int64_t nval = ggml_nelements(in_sum2); if (e.values.empty()) { e.values.resize(nval, 0.0f); + if (in_sum != nullptr) { + e.activations.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); @@ -804,6 +1050,7 @@ bool IMatrixCollector::load_imatrix(const char * file_name) { // Recreate the state as expected by save_imatrix() for (int64_t j = 0; j < nval; j++) { + if (in_sum != nullptr) { e.activations[j] += ((const float *) in_sum->data)[j]; } e.values[j] += ((const float *) in_sum2->data)[j]; } for (int64_t j = 0; j < ncounts; j++) { @@ -1083,104 +1330,148 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params, c static bool show_statistics(const common_params & params) { std::vector ts; + bool legacy_mode = true; + if (params.in_files.empty() || params.in_files.size() > 1) { LOG_ERR("\nError: a single imatrix file is required to compute tensor statistics\n\n"); return false; } if (g_collector.load_imatrix(params.in_files[0].c_str())) { - for (const auto & [name, stats] :g_collector.get_mstats()) { - compute_statistics(ts, name, stats); + for (const auto & [name, stats] : g_collector.get_mstats()) { + const bool is_legacy = compute_vector_statistics(ts, name, stats); + legacy_mode = legacy_mode && is_legacy; } } else { LOG_ERR("\nError: %s is not a valid imatrix file\n\n", params.in_files[0].c_str()); return false; } if (!ts.empty()) { - compute_cossim(ts); + compute_tensor_statistics(ts); } else { LOG_ERR("Error: cannot compute statistics for %s\n\n", params.in_files[0].c_str()); return false; } struct tensor_comparer { + bool legacy_mode; + explicit tensor_comparer(const bool legacy) : legacy_mode(legacy) {} + bool operator()(const tensor_statistics & a, const tensor_statistics & b) const { - std::string layer, name_a, name_b; - ; + std::string layer; + std::string name_a; + std::string name_b; process_tensor_name(a.tensor, layer, name_a); process_tensor_name(b.tensor, layer, name_b); - return name_a < name_b || (name_a == name_b && a.total_sqract > b.total_sqract); + return legacy_mode ? name_a < name_b || (name_a == name_b && a.sum_values > b.sum_values) + : name_a < name_b || (name_a == name_b && a.cossim > b.cossim); } }; - std::sort(ts.begin(), ts.end(), tensor_comparer()); + std::sort(ts.begin(), ts.end(), tensor_comparer(legacy_mode)); - struct weighted_stats { - float weighted_bias = 0.0f; - float weighted_zd = 0.0f; - float weighted_cossim = 0.0f; - int total_elements = 0; + struct layer_stats { + float layer_sum = 0.0f; + float layer_zd = 0.0f; + int n = 0; }; - std::map ws; - LOG_INF("\nComputing statistics for %s (%d tensors)\n", params.in_files[0].c_str(), static_cast(ts.size())); - LOG_INF("\n%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n", " Layer", " Tensor", " Σ(Act²)", - " Min", " Max", " μ", " σ", " % Active", "N", " Entropy", "E (norm)", "ZD", - " CosSim"); + std::map ls; + LOG_INF("\nComputing tensor statistics for %s (%d tensors)\n", params.in_files[0].c_str(), static_cast(ts.size())); + LOG_INF("\n%6s\t%18s\t%13s\t%8s\t%8s\t%7s\t%15s\t%13s\t%11s\t%8s\t%5s\t%10s\n", + "Layer", + "Tensor", + legacy_mode ? "Σ E[Act²]" : "L₂ Dist", + "Min", + "Max", + "μ", + "σ", + "N", + "H Norm", + legacy_mode ? "H" : "ECS", + "ZD", + "CosSim"); LOG_INF( "==============================================================================================================" - "===========================================================\n"); + "=============================================================\n"); + for (const auto & tstat : ts) { - std::string layer, name; + std::string layer; + std::string name; process_tensor_name(tstat.tensor, layer, name); int blk; try { blk = std::stoi(layer); - } catch (const std::exception & e) { - blk = -1; // not a block layer + } catch (const std::exception &) { + blk = -1; // not a block layer } - LOG_INF("%5s\t%-20s\t%10.2f\t%8.4f\t%11.4f\t%6.2f\t%6.2f\t%8.2f%%\t%6d\t%10.4f\t%6.2f%%\t%10.2f%%\t%8.4f\n", - layer.c_str(), name.c_str(), tstat.total_sqract, tstat.min_sqract, tstat.max_sqract, tstat.mean_sqract, - tstat.stddev, tstat.active * 100.0f, tstat.elements, tstat.entropy, - 100.0f * (tstat.entropy / std::log2(tstat.elements)), 100.0f * tstat.zd, tstat.cossim); - - const float weighted_bias = tstat.elements * tstat.total_sqract; - const float weighted_zd = tstat.elements * tstat.zd; - const float weighted_cossim = tstat.elements * tstat.cossim; - - if (ws.find(blk) != ws.end()) { - ws[blk].weighted_bias += weighted_bias; - ws[blk].weighted_zd += weighted_zd; - ws[blk].weighted_cossim += weighted_cossim; - ws[blk].total_elements += tstat.elements; + const float h_norm = tstat.elements > 1 ? 100.0f * (tstat.entropy / std::log2((float) tstat.elements)) : 0.0f; + const float ecs = 100.0f * (1.0f - std::exp(-0.01f * tstat.l2_dist) * std::pow(std::fabs(tstat.cossim), 10.0f)); // Euclidean-Cosine score + + LOG_INF("%5s\t%-20s\t%11.4f\t%10.4f\t%10.4f\t%8.4f\t%8.4f\t%7d\t%10.2f%%\t%10.4f\t%6.2f%%\t%10.4f\n", + layer.c_str(), + name.c_str(), + legacy_mode ? tstat.sum_values : tstat.l2_dist, + tstat.min_values, + tstat.max_values, + tstat.mean_values, + tstat.std_deviation, + tstat.elements, + h_norm, + legacy_mode ? tstat.entropy : ecs, + 100.0f * tstat.zd_score, + tstat.cossim); + + const float zd = tstat.elements * tstat.zd_score; + if (ls.find(blk) != ls.end()) { + ls[blk].layer_sum += tstat.sum_values; + ls[blk].layer_zd += zd; + ls[blk].n += tstat.elements; } else { - weighted_stats temp_ws; - temp_ws.weighted_bias = weighted_bias; - temp_ws.weighted_zd = weighted_zd; - temp_ws.weighted_cossim = weighted_cossim; - temp_ws.total_elements = tstat.elements; - ws[blk] = temp_ws; + layer_stats temp_ls; + temp_ls.layer_sum = tstat.sum_values; + temp_ls.layer_zd = zd; + temp_ls.n = tstat.elements; + ls[blk] = temp_ls; } } - const int layers = std::count_if(ws.begin(), ws.end(), [](const auto & kv) { return kv.first >= 0; }); - LOG_INF("\nComputing weighted average statistics per layer (%d layers)\n", layers); - LOG_INF("\n%s\t%s\t%s\t%s\n", " Layer", " μΣ(Act²)", " μZD", "μCosSim"); - LOG_INF("================================================\n"); - for (const auto & [first, second] : ws) { - const auto & layer = first; - const auto & stats = second; - - if (stats.total_elements == 0) { - continue; - } - - if (layer >= 0) { - const float bias = stats.weighted_bias / stats.total_elements; - const float zd = stats.weighted_zd / stats.total_elements; - const float cossim = stats.weighted_cossim / stats.total_elements; - - LOG_INF("%5d\t%14.2f\t%10.4f%%\t%6.4f\n", layer, bias, 100.0f * zd, cossim); + std::map layer_cossim; + std::map layer_l2_dist; + compute_layer_statistics(ts, layer_cossim, layer_l2_dist, g_collector.get_mstats()); + + const size_t layers = std::count_if(ls.begin(), ls.end(), [](const auto & kv) { return kv.first >= 0; }); + LOG_INF("\nComputing layer statistics (%zu layers)\n", layers); + LOG_INF("\n%6s\t%13s\t%6s\t%11s\t%6s\n", + "Layer", + legacy_mode ? "Σ E[Act²]" : "L₂ Dist", + "ZD", + "CosSim", + legacy_mode ? "" : "ECS"); + if (legacy_mode) { + LOG_INF("============================================\n"); + } else { + LOG_INF("=========================================================\n"); + } + for (const auto & [layer, stats] : ls) { + if (layer < 0 || stats.n == 0) { continue; } + const auto lcs = layer_cossim.find(layer); + const float layer_cs = lcs != layer_cossim.end() ? lcs->second : 0.0f; + const auto ll2n = layer_l2_dist.find(layer); + const float layer_l2n = ll2n != layer_l2_dist.end() ? ll2n->second : 0.0f; + if (legacy_mode) { + LOG_INF("%5d\t%11.4f\t%6.2f%%\t%11.4f\n", + layer, + stats.layer_sum, + 100.0f * stats.layer_zd / stats.n, + layer_cs); + } else { + LOG_INF("%5d\t%11.4f\t%6.2f%%\t%11.4f\t%8.4f\n", + layer, + layer_l2n, + 100.0f * stats.layer_zd / stats.n, + layer_cs, + 100.0f * (1.0f - std::exp(-0.01f * layer_l2n) * std::pow(std::fabs(layer_cs), 10.0f))); // Euclidean-Cosine score } } LOG_INF("\n");