Add quantize-stats command for testing quantization

.gitignore

@@ -19,6 +19,7 @@ models/*
 
 /main
 /quantize
+/quantize-stats
 /result
 /perplexity
 /embedding

Makefile

@@ -148,7 +148,7 @@ common.o: examples/common.cpp examples/common.h
 	$(CXX) $(CXXFLAGS) -c examples/common.cpp -o common.o
 
 clean:
-	rm -vf *.o main quantize perplexity embedding
+	rm -vf *.o main quantize quantize-stats perplexity embedding
 
 main: examples/main/main.cpp ggml.o llama.o common.o
 	$(CXX) $(CXXFLAGS) examples/main/main.cpp ggml.o llama.o common.o -o main $(LDFLAGS)
@@ -159,6 +159,9 @@ main: examples/main/main.cpp ggml.o llama.o common.o
 quantize: examples/quantize/quantize.cpp ggml.o llama.o
 	$(CXX) $(CXXFLAGS) examples/quantize/quantize.cpp ggml.o llama.o -o quantize $(LDFLAGS)
 
+quantize-stats: examples/quantize-stats/quantize-stats.cpp ggml.o llama.o
+	$(CXX) $(CXXFLAGS) examples/quantize-stats/quantize-stats.cpp ggml.o llama.o -o quantize-stats $(LDFLAGS)
+
 perplexity: examples/perplexity/perplexity.cpp ggml.o llama.o common.o
 	$(CXX) $(CXXFLAGS) examples/perplexity/perplexity.cpp ggml.o llama.o common.o -o perplexity $(LDFLAGS)
 

examples/CMakeLists.txt

@@ -31,6 +31,7 @@ if (EMSCRIPTEN)
 else()
     add_subdirectory(main)
     add_subdirectory(quantize)
+    add_subdirectory(quantize-stats)
     add_subdirectory(perplexity)
     add_subdirectory(embedding)
 endif()

examples/quantize-stats/CMakeLists.txt

@@ -0,0 +1,4 @@
+set(TARGET quantize-stats)
+add_executable(${TARGET} quantize-stats.cpp)
+target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)

examples/quantize-stats/quantize-stats.cpp

@@ -0,0 +1,355 @@
+#include "ggml.h"
+#include "llama.h"
+
+#include <algorithm>
+#include <cassert>
+#include <cinttypes>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <map>
+#include <numeric>
+#include <regex>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+static const char * type_strs[] = { "q4_0", "q4_1", "i8", "i16", "i32", "f16", "f32"  };
+static_assert(sizeof(type_strs) == GGML_TYPE_COUNT * sizeof(char *), "Incomplete type list");
+
+struct quantize_stats_params {
+    std::string model = "models/7B/ggml-model-f16.bin";
+    bool verbose = false;
+    bool per_layer_stats = false;
+    bool print_histogram = false;
+    bool reference = false;
+    std::vector<std::string> include_layers;
+    std::vector<std::string> exclude_layers;
+    std::vector<enum ggml_type> include_types;
+};
+
+const int64_t SCRATCH_ELEMENTS = 32*32;
+const size_t HISTOGRAM_BUCKETS = 150;
+const double HISTOGRAM_RANGE = 0.03;
+
+struct error_stats {
+    size_t num_samples;
+    double total_error;
+    double max_error;
+    uint64_t error_histogram[HISTOGRAM_BUCKETS];
+};
+
+
+void quantize_stats_print_usage(int /*argc*/, char ** argv) {
+    quantize_stats_params params;
+    fprintf(stderr, "usage: %s [options]\n", argv[0]);
+    fprintf(stderr, "\n");
+    fprintf(stderr, "options:\n");
+    fprintf(stderr, "  -h, --help            show this help message and exit\n");
+    fprintf(stderr, "  -m FNAME, --model FNAME\n");
+    fprintf(stderr, "                        model path (default: %s)\n", params.model.c_str());
+    fprintf(stderr, "  -r, --reference\n");
+    fprintf(stderr, "                        use reference implementation (default: false)\n");
+    fprintf(stderr, "  -v, --verbose\n");
+    fprintf(stderr, "                        verbose output (default: false)\n");
+    fprintf(stderr, "  -p, --per-layer-stats\n");
+    fprintf(stderr, "                        print stats per layer (default: false)\n");
+    fprintf(stderr, "  --histogram\n");
+    fprintf(stderr, "                        print error histogram (default: false)\n");
+    fprintf(stderr, "  -l LAYER, --include-layer LAYER\n");
+    fprintf(stderr, "                        only test layers matching pattern\n");
+    fprintf(stderr, "  -L LAYER, --exclude-layer LAYER\n");
+    fprintf(stderr, "                        exclude layers matching pattern\n");
+    fprintf(stderr, "  -t TYPE, --type TYPE\n");
+    fprintf(stderr, "                        only test given type (q4_0, q4_1)\n");
+    fprintf(stderr, "\n");
+}
+
+// Check if a layer is included/excluded by command line
+bool layer_included(const quantize_stats_params params, const std::string & layer) {
+    for (const auto& excluded : params.exclude_layers) {
+        if (std::regex_search(layer, std::regex(excluded))) {
+            return false;
+        }
+    }
+    for (const auto& included : params.include_layers) {
+        if (std::regex_search(layer, std::regex(included))) {
+            return true;
+        }
+    }
+    return params.include_layers.empty();
+}
+
+// Update error statistics given vectors with the before/after result of quantization
+void update_error_stats(int64_t nelements, const float * input, const float * output, error_stats & stats) {
+    for (int64_t i = 0; i < nelements; i++) {
+        double diff = input[i] - output[i];
+        stats.total_error += diff * diff;
+        stats.max_error = fmax(fabs(diff), stats.max_error);
+        stats.error_histogram[std::max(std::min((size_t) floor(fabs(diff) / HISTOGRAM_RANGE * HISTOGRAM_BUCKETS), HISTOGRAM_BUCKETS-1), (size_t) 0)]++;
+    }
+    stats.num_samples += nelements;
+}
+
+double find_quantile(const error_stats & stats, double quantile) {
+    double sum = std::accumulate(std::begin(stats.error_histogram), std::end(stats.error_histogram), 0.0);
+
+    double accum = 0;
+    for (size_t i = 0; i < HISTOGRAM_BUCKETS; i++) {
+        accum += stats.error_histogram[i];
+        if (accum >= sum*quantile) {
+            return (i+1) * HISTOGRAM_RANGE / HISTOGRAM_BUCKETS;
+        }
+    }
+    return INFINITY;
+}
+
+void print_error_stats(const std::string & name, const error_stats & stats, bool print_histogram) {
+    double rmse = sqrt(stats.total_error / (double) stats.num_samples);
+    double median = find_quantile(stats, .5);
+    double pct95 = find_quantile(stats, .95);
+    printf("%-50s: rmse %.8f, maxerr %.8f, 95pct<%.4f, median<%.4f\n", name.c_str(), rmse, stats.max_error, pct95, median);
+    if (print_histogram) {
+        printf("Error distribution:\n");
+        for (size_t i = 0; i < HISTOGRAM_BUCKETS; i++) {
+            double lower = i * HISTOGRAM_RANGE / HISTOGRAM_BUCKETS;
+            double upper = (i+1) * HISTOGRAM_RANGE / HISTOGRAM_BUCKETS;
+            if (i == HISTOGRAM_BUCKETS -1) upper = INFINITY;
+            printf("[%3.4f, %3.4f): %11" PRIu64 "\n", lower, upper, stats.error_histogram[i]);
+        }
+    }
+}
+
+// copied from ggml.h - verify that we can access this as a flat array
+static bool tensor_is_contiguous(const struct ggml_tensor * tensor) {
+    static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function");
+
+    return
+        tensor->nb[0] == ggml_type_size(tensor->type) &&
+        tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) &&
+        tensor->nb[2] == tensor->nb[1]*tensor->ne[1] &&
+        tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
+}
+
+// Run quantization function for a single layer and update error stats
+void test_roundtrip_on_layer(
+        std::string & name,
+        bool print_layer_stats,
+        const quantize_fns_t & qfns,
+        bool use_reference,
+        const ggml_tensor * layer,
+        float * input_scratch,
+        char *quantized_scratch,
+        float * output_scratch,
+        error_stats & total_error) {
+
+    assert(tensor_is_contiguous(layer));
+    error_stats layer_error {};
+    int64_t nelements = ggml_nelements(layer);
+
+    for (int64_t offset = 0; offset < nelements; offset += SCRATCH_ELEMENTS) {
+        int64_t chunk_size = std::min(SCRATCH_ELEMENTS, nelements - offset);
+
+        if (layer->type == GGML_TYPE_F16) {
+            for (int i = 0; i < chunk_size; i++) {
+                input_scratch[i] = ggml_get_f32_1d(layer, i + offset);
+            }
+        } else {
+            input_scratch = ggml_get_data_f32(layer) + offset;
+        }
+
+        if (use_reference) {
+            qfns.quantize_row_q_reference(input_scratch, quantized_scratch, chunk_size);
+        } else {
+            qfns.quantize_row_q(input_scratch, quantized_scratch, chunk_size);
+        }
+        qfns.dequantize_row_q(quantized_scratch, output_scratch, chunk_size);
+
+        update_error_stats(chunk_size, input_scratch, output_scratch, total_error);
+        if (print_layer_stats) {
+            update_error_stats(chunk_size, input_scratch, output_scratch, layer_error);
+        }
+    }
+    if (print_layer_stats) {
+        print_error_stats(name, layer_error, false);
+    }
+}
+
+int main(int argc, char ** argv) {
+    ggml_time_init();
+
+    quantize_stats_params params;
+
+    // read command line
+
+    bool invalid_param = false;
+    std::string arg;
+    for (int i = 1; i < argc; i++) {
+        arg = argv[i];
+
+        if (arg == "-h" || arg == "--help") {
+            quantize_stats_print_usage(argc, argv);
+            exit(0);
+        } else if (arg == "-r" || arg == "--reference") {
+            params.reference = true;
+        } else if (arg == "-v") {
+            params.verbose = true;
+        } else if (arg == "-p" || arg == "--per-layer-stats") {
+            params.per_layer_stats = true;
+        } else if (arg == "--histogram") {
+            params.print_histogram = true;
+        } else if (arg == "-m" || arg == "--model") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.model = argv[i];
+        } else if (arg == "-l" || arg == "--include-layer") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.include_layers.push_back(argv[i]);
+        } else if (arg == "-L" || arg == "--exclude-layer") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.exclude_layers.push_back(argv[i]);
+        } else if (arg == "-t" || arg == "--type") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            int j;
+            for (j = 0; j < GGML_TYPE_COUNT && strcmp(argv[i], type_strs[j]) != 0; j++) {
+                // find match
+            }
+            if (j < GGML_TYPE_COUNT) {
+                params.include_types.push_back((ggml_type) j);
+            } else {
+                fprintf(stderr, "error: %s not in list of types\n", argv[i]);
+                invalid_param = true;
+            }
+        } else {
+            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+            quantize_stats_print_usage(argc, argv);
+            return 1;
+        }
+    }
+    if (invalid_param) {
+        fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str());
+        quantize_stats_print_usage(argc, argv);
+        return 1;
+    }
+
+    // load the model
+    fprintf(stderr, "Loading model\n");
+
+    const int64_t t_main_start_us = ggml_time_us();
+    llama_context * ctx;
+
+    {
+        auto lparams = llama_context_default_params();
+
+        lparams.n_ctx      = 256;
+        lparams.n_parts    = 1;
+        lparams.seed       = 1;
+        lparams.f16_kv     = false;
+        lparams.use_mlock  = false;
+
+        ctx = llama_init_from_file(params.model.c_str(), lparams);
+
+        if (ctx == NULL) {
+            fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
+            return 1;
+        }
+    }
+
+    // Sort tensors for consistent output
+    const auto tensors = llama_internal_get_tensor_map(ctx);
+    std::map<std::string, struct ggml_tensor *> tensors_sorted { tensors.begin(), tensors.end() };
+
+    // check layer tensors
+    int included_layers = 0;
+    int64_t max_nelements = 0;
+    bool is_f16 = false;
+    for (const auto& kv_tensor : tensors_sorted) {
+        if (!layer_included(params, kv_tensor.first)) {
+            continue;
+        }
+        if (params.verbose) {
+            printf("%s: type %s, size %" PRId64 "\n", kv_tensor.first.c_str(), type_strs[kv_tensor.second->type], ggml_nelements(kv_tensor.second));
+        }
+        if (kv_tensor.second->type == GGML_TYPE_F16) {
+            is_f16 = true;
+        } else if (kv_tensor.second->type != GGML_TYPE_F32) {
+            fprintf(stderr, "%s: error: Quantization should be tested with a float model, "
+                "this model contains already quantized layers (%s is type %d)\n", __func__, kv_tensor.first.c_str(), kv_tensor.second->type);
+            llama_free(ctx);
+            return 1;
+        }
+        included_layers++;
+        max_nelements = std::max(max_nelements, ggml_nelements(kv_tensor.second));
+    }
+
+    if (is_f16) {
+        printf("note: source model is f16\n");
+    }
+    printf("testing %d layers with max size %" PRId64 "\n", included_layers, max_nelements);
+    // allocate scratch space
+    std::vector<float> input_scratch(SCRATCH_ELEMENTS);
+    std::vector<char> quantized_scratch(SCRATCH_ELEMENTS*4);
+    std::vector<float> output_scratch(SCRATCH_ELEMENTS);
+
+    // loop throught quantization types
+    for (int i = 0; i < GGML_TYPE_COUNT; i++) {
+        if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) {
+            continue;
+        }
+        quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
+        if (qfns.quantize_row_q && qfns.dequantize_row_q) {
+            if (params.verbose) {
+                printf("testing %s ...\n",  type_strs[i]);
+            }
+
+            error_stats global_stats {};
+
+            for (const auto& kv_tensor : tensors_sorted) {
+                if (!layer_included(params, kv_tensor.first)) {
+                    continue;
+                }
+                if (params.verbose) {
+                    printf("  %s ...\n",  kv_tensor.first.c_str());
+                }
+                std::string layer_name { type_strs[i] };
+                layer_name += "::" + kv_tensor.first;
+                test_roundtrip_on_layer(
+                        layer_name,
+                        params.per_layer_stats,
+                        qfns,
+                        params.reference,
+                        kv_tensor.second,
+                        input_scratch.data(),
+                        quantized_scratch.data(),
+                        output_scratch.data(),
+                        global_stats
+                );
+            }
+
+            print_error_stats(type_strs[i], global_stats, params.print_histogram);
+        }
+    }
+
+
+    llama_free(ctx);
+    // report timing
+    {
+        const int64_t t_main_end_us = ggml_time_us();
+
+        printf("\n");
+        printf("%s:    total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0);
+    }
+
+    return 0;
+}