Remove activation_statistics() option

Author: EAddario

common/arg.cpp

@@ -2758,16 +2758,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.show_statistics = true;
         }
     ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
-    add_opt(common_arg(
-        {"--activation-statistics"},
-        string_format("generate data to compute activation-based statistics (default: %s)", params.show_statistics ? "true" : "false"),
-        [](common_params & params) {
-            params.activation_statistics = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
     add_opt(common_arg(
         {"--parse-special"},
-        string_format("prase special tokens (chat, tool, etc) (default: %s)", params.parse_special ? "true" : "false"),
+        string_format("parse special tokens (chat, tool, etc) (default: %s)", params.parse_special ? "true" : "false"),
         [](common_params & params) {
             params.parse_special = true;
         }

tools/imatrix/imatrix.cpp

@@ -30,7 +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"
-            "       [--output-format gguf|dat] [--activation-statistics] [--show-statistics] [...]\n" , argv[0]);
+            "       [--output-format gguf|dat] [--show-statistics] [...]\n" , argv[0]);
     LOG("\n");
 }
 
@@ -63,7 +63,6 @@ class IMatrixCollector {
 public:
     IMatrixCollector() = default;
     void set_params(common_params params) { m_params = std::move(params); }
-    bool activation_statistics() const { return m_params.activation_statistics; }
     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;
@@ -434,7 +433,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
             e.counts.resize(n_as, e.counts[0]);
         }
         if (e.values.empty()) {
-            if (activation_statistics()) { e.activations.resize(src1->ne[0]*n_as, 0); }
+            e.activations.resize(src1->ne[0]*n_as, 0);
             e.values.resize(src1->ne[0]*n_as, 0);
             e.counts.resize(n_as, 0);
         }
@@ -466,7 +465,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
                     e.counts[ex]++;
 
                     for (int64_t j = 0; j < src1->ne[0]; ++j) {
-                        if (activation_statistics()) { e.activations[e_start + j] += x[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());
@@ -506,7 +505,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
             }
         }
         if (e.values.empty()) {
-            if (activation_statistics()) { e.activations.resize(src1->ne[0] * n_mat, 0); }
+            e.activations.resize(src1->ne[0] * n_mat, 0);
             e.values.resize(src1->ne[0] * n_mat, 0);
             e.counts.resize(1, 0);
         }
@@ -525,7 +524,7 @@ 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) {
-                        if (activation_statistics()) { e.activations[mat_start + j] += x[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());
@@ -707,7 +706,7 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
         }
 
         to_store.push_back(kv.first);
-        if (activation_statistics()) { 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.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);
     }
@@ -761,7 +760,7 @@ 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() && activation_statistics()) {
+            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());