Merge pull request #74 from ggerganov/master

b1961

.devops/main-intel.Dockerfile

@@ -0,0 +1,26 @@
+ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04
+ARG UBUNTU_VERSION=22.04
+
+FROM intel/hpckit:$ONEAPI_VERSION as build
+
+RUN apt-get update && \
+    apt-get install -y git
+
+WORKDIR /app
+
+COPY . .
+
+# for some reasons, "-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DLLAMA_NATIVE=ON" give worse performance
+RUN mkdir build && \
+    cd build && \
+    cmake .. -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx && \
+    cmake --build . --config Release --target main server
+
+FROM ubuntu:$UBUNTU_VERSION as runtime
+
+COPY --from=build /app/build/bin/main /main
+COPY --from=build /app/build/bin/server /server
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/main" ]

.devops/nix/package.nix

@@ -225,6 +225,9 @@ effectiveStdenv.mkDerivation (
         description = "contains numpy and sentencepiece";
         buildInputs = [ llama-python ];
         inputsFrom = [ finalAttrs.finalPackage ];
+        shellHook = ''
+          addToSearchPath "LD_LIBRARY_PATH" "${lib.getLib effectiveStdenv.cc.cc}/lib"
+        '';
       };
 
       shell-extra = mkShell {

.github/workflows/docker.yml

@@ -35,6 +35,7 @@ jobs:
           - { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" }
           - { tag: "light-rocm", dockerfile: ".devops/main-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
           - { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          - { tag: "light-intel", dockerfile: ".devops/main-intel.Dockerfile", platforms: "linux/amd64" }
     steps:
       - name: Check out the repo
         uses: actions/checkout@v3

CMakeLists.txt

@@ -478,6 +478,11 @@ function(get_flags CCID CCVER)
         if (CCVER VERSION_GREATER_EQUAL 8.1.0)
             set(CXX_FLAGS ${CXX_FLAGS} -Wextra-semi)
         endif()
+    elseif (CCID MATCHES "Intel")
+        # enable max optimization level when using Intel compiler
+        set(C_FLAGS   -ipo -O3 -static -fp-model=fast -flto -fno-stack-protector)
+        set(CXX_FLAGS -ipo -O3 -static -fp-model=fast -flto -fno-stack-protector)
+        add_link_options(-fuse-ld=lld -static-intel)
     endif()
 
     set(GF_C_FLAGS   ${C_FLAGS}   PARENT_SCOPE)

common/common.cpp

@@ -216,12 +216,10 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
             }
             // store the external file name in params
             params.prompt_file = argv[i];
-            file.seekg(0, std::ios::end);
-            size_t size = file.tellg();
-            file.seekg(0, std::ios::beg);
-            params.prompt.resize(size);
-            file.read((char *)params.prompt.data(), size);
-            fprintf(stderr, "Read %zu bytes from binary file %s\n", size, argv[i]);
+            std::ostringstream ss;
+            ss << file.rdbuf();
+            params.prompt = ss.str();
+            fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), argv[i]);
         } else if (arg == "-f" || arg == "--file") {
             if (++i >= argc) {
                 invalid_param = true;

examples/llama.vim

@@ -6,7 +6,7 @@
 " Similarly, you could add an insert mode keybind with
 " inoremap <C-B> <Cmd>call llama#doLlamaGen()<CR>
 "
-" g:llama_api_url and g:llama_overrides can be configured in your .vimrc
+" g:llama_api_url, g:llama_api_key and g:llama_overrides can be configured in your .vimrc
 " let g:llama_api_url = "192.168.1.10:8080"
 " llama_overrides can also be set through buffer/window scopes. For instance
 " autocmd filetype python let b:llama_overrides = {"temp": 0.2}
@@ -82,6 +82,9 @@ func llama#doLlamaGen()
    endif
    let l:querydata.prompt = join(l:buflines, "\n")
    let l:curlcommand = copy(s:curlcommand)
+   if exists("g:llama_api_key")
+       call extend(l:curlcommand, ['--header', 'Authorization: Bearer ' .. g:llama_api_key])
+   endif
    let l:curlcommand[2] = json_encode(l:querydata)
    let b:job = job_start(l:curlcommand, {"callback": function("s:callbackHandler", [l:cbuffer])})
 endfunction

examples/llava/clip.cpp

@@ -2,18 +2,6 @@
 // so there might be still unnecessary artifacts hanging around
 // I'll gradually clean and extend it
 
-#include <cassert>
-#include <cmath>
-#include <cstdlib>
-#include <cstring>
-#include <fstream>
-#include <iostream>
-#include <map>
-#include <regex>
-#include <stdexcept>
-#include <vector>
-#include <sstream>
-
 #include "clip.h"
 #include "ggml.h"
 #include "ggml-alloc.h"
@@ -30,6 +18,19 @@
 #define STB_IMAGE_IMPLEMENTATION
 #include "stb_image.h"
 
+#include <cassert>
+#include <cmath>
+#include <cstdlib>
+#include <cstring>
+#include <fstream>
+#include <iostream>
+#include <map>
+#include <regex>
+#include <stdexcept>
+#include <vector>
+#include <sstream>
+#include <cinttypes>
+
 static std::string format(const char * fmt, ...) {
     va_list ap;
     va_list ap2;
@@ -217,9 +218,9 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) {
 
 static void print_tensor_info(const ggml_tensor* tensor, const char* prefix = "") {
     size_t tensor_size = ggml_nbytes(tensor);
-    printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%d, %d, %d, %d], type: %d\n",
+    printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n",
             prefix, ggml_n_dims(tensor), tensor->name, tensor_size,
-            tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->type);
+            tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], ggml_type_name(tensor->type));
 }
 
 static projector_type clip_projector_type_from_string(const std::string & name) {
@@ -592,7 +593,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
                 mlp_3 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_3, 1, 0, 2, 3));
                 mlp_3 = ggml_reshape_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]);
                 // stride = 1, padding = 1, bias is nullptr
-                block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, nullptr, 1, 1, 1, 1, 1, 1);
+                block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1);
 
                 // layer norm
                 // // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
@@ -640,7 +641,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             // block_2
             {
                 // stride = 2
-                block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_2_block_0_0_w, block_1, nullptr, 2, 2, 1, 1, 1, 1);
+                block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1);
 
                 // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1]
                 // layer norm
@@ -741,18 +742,10 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
     {
         std::map<enum ggml_type, uint32_t> n_type;
 
-        uint32_t n_type_max = 0;
-        enum ggml_type type_max = GGML_TYPE_F32;
-
         for (int i = 0; i < n_tensors; i++) {
             enum ggml_type type = gguf_get_tensor_type(ctx, i);
 
             n_type[type]++;
-
-            if (n_type_max < n_type[type]) {
-                n_type_max = n_type[type];
-                type_max   = type;
-            }
         }
 
         printf("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__);
@@ -795,14 +788,12 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             size_t tensor_size = ggml_nbytes(cur);
             buffer_size += tensor_size;
             if (verbosity >= 3) {
-                printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%d, %d, %d, %d], type: %d\n", __func__, i,
-                       ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], type);
+                printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n",
+                       __func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type));
             }
         }
     }
 
-
-
     buffer_size += n_tensors * 128 /* CLIP PADDING */;
 
     clip_ctx * new_clip = new clip_ctx;

examples/perplexity/perplexity.cpp

@@ -222,13 +222,18 @@ struct kl_divergence_result {
     double sum_kld2 = 0;
     double sum_nll_diff  = 0;
     double sum_nll_diff2 = 0;
+    size_t n_same_top = 0;
     size_t count = 0;
 };
 
-static void log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) {
+static double log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) {
     float max_logit = logits[0];
+    int imax = 0;
     for (int i = 1; i < n_vocab; ++i) {
-        max_logit = std::max(max_logit, logits[i]);
+        if (logits[i] > max_logit) {
+            max_logit = logits[i];
+            imax = i;
+        }
     }
     double sum_exp = 0.0;
     for (int i = 0; i < n_vocab; ++i) {
@@ -247,8 +252,14 @@ static void log_softmax(int n_vocab, const float * logits, const uint16_t * base
     kld.sum_nll_diff2 += nll*nll;
     max_logit += log_sum_exp;
     double sum = 0;
+    int imax_base = -1;
+    float p_log_base_max = 0;
     for (int i = 0; i < n_vocab; ++i) {
         const float p_log_base = scale*base_log_prob[i] + min_log_prob;
+        if (i == 0 || p_log_base > p_log_base_max) {
+            p_log_base_max = p_log_base;
+            imax_base = i;
+        }
         if (p_log_base > -16.f) {
             const float p_base = expf(p_log_base);
             sum += p_base * (p_log_base - logits[i] + max_logit);
@@ -257,14 +268,17 @@ static void log_softmax(int n_vocab, const float * logits, const uint16_t * base
     kld.sum_kld  += sum;
     kld.sum_kld2 += sum*sum;
     ++kld.count;
+    if (imax == imax_base) ++kld.n_same_top;
+    return sum;
 }
 
 static void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token,
-        std::vector<std::thread> & workers, const std::vector<uint16_t> & base_log_probs, kl_divergence_result & kld) {
+        std::vector<std::thread> & workers, const std::vector<uint16_t> & base_log_probs, kl_divergence_result & kld,
+        float * kld_values) {
     std::mutex mutex;
     const int nv = 2*((n_vocab + 1)/2) + 4;
     int counter = 0;
-    auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv] () {
+    auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv, kld_values] () {
         kl_divergence_result local_kld;
         while (true) {
             std::unique_lock<std::mutex> lock(mutex);
@@ -276,11 +290,13 @@ static void process_logits(int n_vocab, const float * logits, const int * tokens
                 kld.sum_kld2 += local_kld.sum_kld2;
                 kld.sum_nll_diff  += local_kld.sum_nll_diff;
                 kld.sum_nll_diff2 += local_kld.sum_nll_diff2;
+                kld.n_same_top += local_kld.n_same_top;
                 kld.count += local_kld.count;
                 break;
             }
             lock.unlock();
-            log_softmax(n_vocab, logits + i*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld);
+            double v = log_softmax(n_vocab, logits + i*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld);
+            kld_values[i] = (float)v;
         }
     };
     for (auto & w : workers) {
@@ -1202,11 +1218,11 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
     printf("Final Winogrande score(%d tasks): %.4lf +/- %.4lf\n", n_done, 100*p, sigma);
 }
 
-static bool deserialize_string(std::istream& in, std::string& str) {
+static bool deserialize_string(std::istream & in, std::string & str) {
     uint32_t size;
     if (!in.read((char *)&size, sizeof(size)).fail()) {
         str.resize(size);
-        if (!in.read((char *)str.data(), size).fail()) return true;
+        if (!in.read((char *)&str[0], size).fail()) return true;
     }
     return false;
 }
@@ -1615,7 +1631,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
     in.read((char *)&n_vocab, sizeof(n_vocab));
     in.read((char *)&n_chunk, sizeof(n_chunk));
     if (in.fail()) {
-        fprintf(stderr, "%s: failed rwading n_vocab, n_chunk from %s\n", __func__, params.logits_file.c_str());
+        fprintf(stderr, "%s: failed reading n_vocab, n_chunk from %s\n", __func__, params.logits_file.c_str());
         return;
     }
     if (n_vocab != llama_n_vocab(llama_get_model(ctx))) {
@@ -1634,6 +1650,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
     const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
 
     std::vector<uint16_t> log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv);
+    std::vector<float> kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk);
     std::vector<float> logits;
     if (num_batches > 1) {
         logits.reserve(n_ctx * n_vocab);
@@ -1652,6 +1669,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
     };
 
     kl_divergence_result kld;
+    auto kld_ptr = kld_values.data();
 
     for (int i = 0; i < n_chunk; ++i) {
         const int start =     i * n_ctx;
@@ -1705,27 +1723,60 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
             }
             fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
 
-            printf("\nchunk        PPL          ln(PPL(Q)/PPL(base))          KL-Divergence\n");
+            printf("\nchunk        PPL          ln(PPL(Q)/PPL(base))          KL-Divergence           Same top\n");
         }
 
         const int first = n_ctx/2;
         const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
         process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
-                workers, log_probs_uint16, kld);
+                workers, log_probs_uint16, kld, kld_ptr);
+        kld_ptr += n_ctx - 1 - first;
 
         auto ppl           = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count);
         auto log_ppl_ratio = mean_and_uncertainty(kld.sum_nll_diff, kld.sum_nll_diff2, kld.count);
         auto kl_div        = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count);
+        auto p_top = 1.*kld.n_same_top/kld.count;
+        auto d_p_top = sqrt(p_top*(1 - p_top)/(kld.count - 1));
 
-        printf("%4d    %10.4lf    %10.5lf ± %10.5f    %10.5f ± %10.5lf\n", i+1, exp(ppl.first),
-                log_ppl_ratio.first, log_ppl_ratio.second, kl_div.first, kl_div.second);
+        printf("%4d    %10.4lf    %10.5lf ± %10.5f    %10.5f ± %10.5lf    %.5f ± %.5f\n", i+1, exp(ppl.first),
+                log_ppl_ratio.first, log_ppl_ratio.second, kl_div.first, kl_div.second,
+                p_top, d_p_top);
 
         fflush(stdout);
 
         logits.clear();
     }
     printf("\n");
 
+    if (kld.count < 100) return; // we do not wish to do statistics on so few values
+
+    std::sort(kld_values.begin(), kld_values.end());
+
+    printf("===== KL-divergence statistics\n");
+    auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count);
+    printf("Average: %10.6f ±%10.6lf\n", kl_div.first, kl_div.second);
+    auto kld_median = kld_values.size()%2 == 0 ? 0.5f*(kld_values[kld_values.size()/2] + kld_values[kld_values.size()/2-1])
+                                               : kld_values[kld_values.size()/2];
+    printf("Median : %10.6f\n", kld_median);
+
+    auto percentile = [&kld_values] (float fraction) {
+        if (fraction <= 0) return kld_values.front();
+        if (fraction >= 1) return kld_values.back();
+        float p = fraction*(kld_values.size() - 1);
+        size_t ip = size_t(p); p -= ip;
+        return (1 - p)*kld_values[ip] + p*kld_values[std::min(ip+1, kld_values.size()-1)];
+    };
+
+    printf("Maximum: %10.6f\n", kld_values.back());
+    printf("KLD_99 : %10.6f\n", percentile(0.99f));
+    printf("KLD_95 : %10.6f\n", percentile(0.95f));
+    printf("KLD_90 : %10.6f\n", percentile(0.90f));
+
+    printf("Minimum: %10.6f\n", kld_values.front());
+    printf("KLD_01 : %10.6f\n", percentile(0.01f));
+    printf("KLD_05 : %10.6f\n", percentile(0.05f));
+    printf("KLD_10 : %10.6f\n", percentile(0.10f));
+
 }
 
 int main(int argc, char ** argv) {

ggml-alloc.c

@@ -109,8 +109,8 @@ void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) {
         if (block->size >= size) {
             best_fit_block = alloc->n_free_blocks - 1;
         } else {
-            fprintf(stderr, "%s: not enough space in the buffer (needed %zu, largest block available %zu)\n",
-                    __func__, size, max_avail);
+            fprintf(stderr, "%s: not enough space in the buffer to allocate %s (needed %zu, largest block available %zu)\n",
+                    __func__, tensor->name, size, max_avail);
             GGML_ASSERT(!"not enough space in the buffer");
             return;
         }