Add quantize-stats command for testing quantization (#728)

Command that calculates some statistics over the errors introduced by
quantization, like mean square error, max error and some percentile errors for layer
weights. Should be useful for testing quantization improvements.

Exposes some internal state from ggml and llama for testing

.gitignore

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

Makefile

@@ -149,7 +149,7 @@ 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)
@@ -160,6 +160,9 @@ 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;
+}

ggml.c

@@ -6564,29 +6564,27 @@ static void ggml_compute_forward_mul_mat_f16_f32(
     //}
 }
 
-typedef void (*dequantize_row_q_t)(const void * restrict x, float * restrict y, int k);
-typedef void (*quantize_row_q_t)(const float * restrict x, void * restrict y, int k);
-typedef void (*vec_dot_q_t)(const int n, float * restrict s, const void * restrict x, const void * restrict y);
-
-typedef struct {
-    dequantize_row_q_t dequantize_row_q;
-    quantize_row_q_t   quantize_row_q;
-    vec_dot_q_t        vec_dot_q;
-} quantize_fns_t;
-
 static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = {
     [GGML_TYPE_Q4_0] = {
         .dequantize_row_q         = dequantize_row_q4_0,
         .quantize_row_q           = quantize_row_q4_0,
+        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_0_reference,
         .vec_dot_q                = ggml_vec_dot_q4_0,
     },
     [GGML_TYPE_Q4_1] = {
         .dequantize_row_q         = dequantize_row_q4_1,
         .quantize_row_q           = quantize_row_q4_1,
+        .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_1_reference,
         .vec_dot_q                = ggml_vec_dot_q4_1,
     },
 };
 
+// For internal test use
+quantize_fns_t ggml_internal_get_quantize_fn(size_t i) {
+    GGML_ASSERT(i < GGML_TYPE_COUNT);
+    return quantize_fns[i];
+}
+
 static void ggml_compute_forward_mul_mat_q_f32(
         const struct ggml_compute_params * params,
         const struct ggml_tensor * src0,

ggml.h

@@ -783,6 +783,30 @@ int ggml_cpu_has_blas(void);
 int ggml_cpu_has_sse3(void);
 int ggml_cpu_has_vsx(void);
 
+
+//
+// Internal types and functions exposed for tests and benchmarks
+//
+
+#ifdef  __cplusplus
+// restrict not standard in C++
+#define GGML_RESTRICT
+#else
+#define GGML_RESTRICT restrict
+#endif
+typedef void (*dequantize_row_q_t)(const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
+typedef void (*quantize_row_q_t)(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
+typedef void (*vec_dot_q_t)(const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y);
+
+typedef struct {
+    dequantize_row_q_t dequantize_row_q;
+    quantize_row_q_t   quantize_row_q;
+    quantize_row_q_t   quantize_row_q_reference;
+    vec_dot_q_t        vec_dot_q;
+} quantize_fns_t;
+
+quantize_fns_t ggml_internal_get_quantize_fn(size_t i);
+
 #ifdef  __cplusplus
 }
 #endif

llama.cpp

@@ -1852,3 +1852,8 @@ const char * llama_print_system_info(void) {
 
     return s.c_str();
 }
+
+// For internal test use
+std::unordered_map<std::string, struct ggml_tensor *>& llama_internal_get_tensor_map(struct llama_context * ctx) {
+    return ctx->model.tensors;
+}

llama.h

@@ -164,6 +164,13 @@ extern "C" {
 
 #ifdef __cplusplus
 }
+
+#include <string>
+#include <unordered_map>
+//
+// Internal function exposed for tests and benchmarks
+//
+std::unordered_map<std::string, struct ggml_tensor *>& llama_internal_get_tensor_map(struct llama_context * ctx);
 #endif
 
 #endif