Better model info (#84)

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

2 files changed, 73 insertions, 6 deletions

diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp
index 6264deb4..88a7d2b9 100644
--- a/examples/quantize-stats/quantize-stats.cpp
+++ b/examples/quantize-stats/quantize-stats.cpp
@@ -17,6 +17,7 @@
 #include <vector>
 #include <thread>
 #include <mutex>
+#include <array>
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
@@ -227,6 +228,31 @@ static void test_roundtrip_on_layer(
     }
 }
 
+static void print_fp_stats(const char * msg, const uint64_t * counts) {
+    printf("===== %s\n", msg);
+    uint64_t tot = 0; for (int i = 0; i < 32; ++i) tot += counts[i];
+    double norm = 1./tot;
+    for (int i = 0; i < 32; ++i) {
+        if (!counts[i]) continue;
+        uint16_t val = i << 10;
+        float f = ggml_fp16_to_fp32(val);
+        printf("%2d    %f   %g\n", i, norm*counts[i], f);
+    }
+}
+
+static void analyze_tensor_fp(const ggml_tensor * t, uint64_t * H) {
+    if (t->type != GGML_TYPE_F16) return;
+    if (!ggml_is_contiguous(t)) return;
+    int n = ggml_nelements(t);
+    const uint16_t * x = (const uint16_t *)t->data;
+    std::array<uint64_t, 32> counts = {};
+    for (int j = 0; j < n; ++j) {
+        ++counts[(x[j] >> 10) & 31];
+    }
+    for (int i = 0; i < 32; ++i) H[i] += counts[i];
+    print_fp_stats(t->name, counts.data());
+}
+
 int main(int argc, char ** argv) {
     ggml_time_init();
 
@@ -236,6 +262,7 @@ int main(int argc, char ** argv) {
 
     int max_thread = 0;
     bool invalid_param = false;
+    bool analyze_fp = false;
     std::string arg;
     for (int i = 1; i < argc; i++) {
         arg = argv[i];
@@ -249,6 +276,8 @@ int main(int argc, char ** argv) {
             params.verbose = true;
         } else if (arg == "-p" || arg == "--per-layer-stats") {
             params.per_layer_stats = true;
+        } else if (arg == "-afp" || arg == "--analyze-fp") {
+            analyze_fp = true;
         } else if (arg == "--histogram") {
             params.print_histogram = true;
         } else if (arg == "-m" || arg == "--model") {
@@ -375,6 +404,22 @@ int main(int argc, char ** argv) {
     std::vector<char> quantized_scratch;
     std::vector<float> output_scratch;
 
+    if (analyze_fp) {
+        for (const auto& kv_tensor : tensors) {
+            if (!layer_included(params, kv_tensor.first)) {
+                continue;
+            }
+            if (kv_tensor.second->ne[0] == 1 || kv_tensor.second->ne[1] == 1) {
+                // we never quantize those
+                continue;
+            }
+            std::array<uint64_t, 32> H = {};
+            analyze_tensor_fp(kv_tensor.second, H.data());
+            print_fp_stats("Total", H.data());
+        }
+        return 0;
+    }
+
     // loop throught quantization types
     for (int i = 0; i < GGML_TYPE_COUNT; i++) {
         const ggml_type type = (ggml_type) i;
diff --git a/src/llama.cpp b/src/llama.cpp
index 80104303..789e2a7c 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -5882,18 +5882,40 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     LLAMA_LOG_INFO("%s: model type       = %s\n",     __func__, llama_model_type_name(model.type));
     LLAMA_LOG_INFO("%s: model ftype      = %s\n",     __func__, llama_model_ftype_name(model.ftype).c_str());
     if (ml.n_elements >= 1e12) {
-        LLAMA_LOG_INFO("%s: model params     = %.2f T\n", __func__, ml.n_elements*1e-12);
+        LLAMA_LOG_INFO("%s: model params     = %.3f T\n", __func__, ml.n_elements*1e-12);
     } else if (ml.n_elements >= 1e9) {
-        LLAMA_LOG_INFO("%s: model params     = %.2f B\n", __func__, ml.n_elements*1e-9);
+        LLAMA_LOG_INFO("%s: model params     = %.3f B\n", __func__, ml.n_elements*1e-9);
     } else if (ml.n_elements >= 1e6) {
-        LLAMA_LOG_INFO("%s: model params     = %.2f M\n", __func__, ml.n_elements*1e-6);
+        LLAMA_LOG_INFO("%s: model params     = %.3f M\n", __func__, ml.n_elements*1e-6);
     } else {
-        LLAMA_LOG_INFO("%s: model params     = %.2f K\n", __func__, ml.n_elements*1e-3);
+        LLAMA_LOG_INFO("%s: model params     = %.3f K\n", __func__, ml.n_elements*1e-3);
     }
     if (ml.n_bytes < GiB) {
-        LLAMA_LOG_INFO("%s: model size       = %.2f MiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0,        ml.n_bytes*8.0/ml.n_elements);
+        LLAMA_LOG_INFO("%s: model size       = %.3f MiB (%.3f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0,        ml.n_bytes*8.0/ml.n_elements);
     } else {
-        LLAMA_LOG_INFO("%s: model size       = %.2f GiB (%.2f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
+        LLAMA_LOG_INFO("%s: model size       = %.3f GiB (%.3f BPW) \n", __func__, ml.n_bytes/1024.0/1024.0/1024.0, ml.n_bytes*8.0/ml.n_elements);
+    }
+    {
+        auto n_bytes = ml.n_bytes;
+        auto n_elements = ml.n_elements;
+        auto meta_tke = ml.get_tensor_meta("token_embd.weight");
+        auto meta_out = ml.get_tensor_meta("output.weight");
+        if (meta_tke && meta_out) {
+            n_bytes -= ggml_nbytes(meta_tke);
+            n_elements -= ggml_nelements(meta_tke);
+            n_bytes -= ggml_nbytes(meta_out);
+            n_elements -= ggml_nelements(meta_out);
+            if (n_bytes < GiB) {
+                LLAMA_LOG_INFO("%s: repeating layers = %.3f MiB (%.3f BPW", __func__, n_bytes/1024.0/1024.0,        n_bytes*8.0/n_elements);
+            } else {
+                LLAMA_LOG_INFO("%s: repeating layers = %.3f GiB (%.3f BPW", __func__, n_bytes/1024.0/1024.0/1024.0, n_bytes*8.0/n_elements);
+            }
+            if (ml.n_elements >= 1e9) {
+                printf(", %.3f B parameters)\n", n_elements*1e-9);
+            } else {
+                printf(", %.3f M parameters)\n", n_elements*1e-6);
+            }
+        }
     }
 
     // general kv