Uncached model read

common/arg.cpp

@@ -1984,6 +1984,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.use_mmap = value;
         }
     ).set_env("LLAMA_ARG_MMAP"));
+    add_opt(common_arg(
+                {"--mmap"},
+                "memory-map model",
+                [](common_params & params) {
+                    params.use_mmap = true;
+                }
+                ).set_env("LLAMA_ARG_MMAP"));
     add_opt(common_arg(
         {"--numa"}, "TYPE",
         "attempt optimizations that help on some NUMA systems\n"

common/common.h

@@ -413,7 +413,7 @@ struct common_params {
     bool kv_unified        = false; // enable unified KV cache
 
     bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
-    bool use_mmap          = true;  // use mmap for faster loads
+    bool use_mmap          = false; // use uncached reads for faster loads
     bool use_mlock         = false; // use mlock to keep model in memory
     bool verbose_prompt    = false; // print prompt tokens before generation
     bool display_prompt    = true;  // print prompt before generation

src/llama-mmap.cpp

@@ -13,9 +13,10 @@
 #ifdef __has_include
     #if __has_include(<unistd.h>)
         #include <unistd.h>
+        #include <fcntl.h>
+        #include <sys/stat.h>
         #if defined(_POSIX_MAPPED_FILES)
             #include <sys/mman.h>
-            #include <fcntl.h>
         #endif
         #if defined(_POSIX_MEMLOCK_RANGE)
             #include <sys/resource.h>
@@ -158,6 +159,129 @@ struct llama_file::impl {
             std::fclose(fp);
         }
     }
+#elif defined(__linux__)
+    impl(const char * fname, const char * mode) : impl(fname, mode, false) {} 
+
+    impl(const char * fname, const char * mode, bool uncached_read) {
+        if (uncached_read) {
+            fd = open(fname, O_RDONLY | O_DIRECT);
+            if (fd == -1) {
+                throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
+            }
+
+            struct stat file_stats{};
+            fstat(fd, &file_stats);
+
+            size = file_stats.st_size;
+
+            off_t ret = lseek(fd, 0, SEEK_SET);
+            if (ret == -1) {
+                throw std::runtime_error(format("seek error: %s", strerror(errno)));
+            }
+        } else {
+            fp = ggml_fopen(fname, mode);
+            if (fp == NULL) {
+                throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
+            }
+            seek(0, SEEK_END);
+            size = tell();
+            seek(0, SEEK_SET);
+        }
+    }
+
+    size_t tell() const {
+        if (fd == -1) {
+            long ret = std::ftell(fp);
+            if (ret == -1) {
+                throw std::runtime_error(format("ftell error: %s", strerror(errno)));
+            }
+
+            return (size_t) ret;
+        }
+
+        off_t pos = lseek(fd, 0, SEEK_CUR);
+        if (pos == -1) {
+            throw std::runtime_error(format("lseek error: %s", strerror(errno)));
+        }
+        return (size_t) pos;
+    }
+
+    void seek(size_t offset, int whence) const {
+        off_t ret = 0;
+        if (fd == -1) {
+            ret = std::fseek(fp, (long) offset, whence);
+        } else {
+            ret = lseek(fd, offset, whence);
+        }
+        if (ret == -1) {
+            throw std::runtime_error(format("seek error: %s", strerror(errno)));
+        }
+    }
+
+    void read_raw(void * ptr, size_t len) const {
+        if (len == 0) {
+            return;
+        }
+        if (fd == -1) {
+            errno = 0;
+            std::size_t ret = std::fread(ptr, len, 1, fp);
+            if (ferror(fp)) {
+                throw std::runtime_error(format("read error: %s", strerror(errno)));
+            }
+            if (ret != 1) {
+                throw std::runtime_error("unexpectedly reached end of file");
+            }
+        } else {
+            bool successful = false;
+            while (!successful) {
+                off_t ret = read(fd, ptr, len);
+
+                if (ret == -1) {
+                    if (errno == EINTR) {
+                        continue;  // Interrupted by signal, retry
+                    }
+                    throw std::runtime_error(format("read error: %s", strerror(errno)));
+                }
+                if (ret == 0) {
+                    throw std::runtime_error("unexpectedly reached end of file");
+                }
+
+                successful = true;
+            }
+        }
+    }
+
+    uint32_t read_u32() const {
+        uint32_t ret;
+        read_raw(&ret, sizeof(ret));
+        return ret;
+    }
+
+    void write_raw(const void * ptr, size_t len) const {
+        if (len == 0) {
+            return;
+        }
+        errno = 0;
+        size_t ret = std::fwrite(ptr, len, 1, fp);
+        if (ret != 1) {
+            throw std::runtime_error(format("write error: %s", strerror(errno)));
+        }
+    }
+
+    void write_u32(uint32_t val) const {
+        write_raw(&val, sizeof(val));
+    }
+
+    ~impl() {
+        if (fp) {
+            std::fclose(fp);
+        } else if (fd != -1) {
+            close(fd);
+        }
+    }
+
+    int fd = -1;
+
 #else
     impl(const char * fname, const char * mode) {
         fp = ggml_fopen(fname, mode);
@@ -237,11 +361,14 @@ struct llama_file::impl {
     }
 #endif
 
-    FILE * fp;
+    FILE * fp{};
-    size_t size;
+    size_t size{};
 };
 
 llama_file::llama_file(const char * fname, const char * mode) : pimpl(std::make_unique<impl>(fname, mode)) {}
+#if defined(__linux__)
+llama_file::llama_file(const char * fname, const char * mode, bool uncached_read) : pimpl(std::make_unique<impl>(fname, mode, uncached_read)) {}
+#endif
 llama_file::~llama_file() = default;
 
 size_t llama_file::tell() const { return pimpl->tell(); }

src/llama-mmap.h

@@ -14,6 +14,9 @@ using llama_mlocks = std::vector<std::unique_ptr<llama_mlock>>;
 
 struct llama_file {
     llama_file(const char * fname, const char * mode);
+#if defined(__linux__)
+    llama_file(const char * fname, const char * mode, bool uncached_read);
+#endif
     ~llama_file();
 
     size_t tell() const;

src/llama-model-loader.cpp

@@ -503,7 +503,11 @@ llama_model_loader::llama_model_loader(
     get_key(llm_kv(LLM_KV_GENERAL_ARCHITECTURE), arch_name, false);
     llm_kv = LLM_KV(llm_arch_from_string(arch_name));
     
+#if defined(__linux__)
+    files.emplace_back(new llama_file(fname.c_str(), "rb", !use_mmap));
+#else
     files.emplace_back(new llama_file(fname.c_str(), "rb"));
+#endif
     contexts.emplace_back(ctx);
 
     // Save tensors data offset of the main file.
@@ -571,7 +575,11 @@ llama_model_loader::llama_model_loader(
                 }
             }
 
+#if defined(__linux__)
+            files.emplace_back(new llama_file(fname_split, "rb", !use_mmap));
+#else
             files.emplace_back(new llama_file(fname_split, "rb"));
+#endif
             contexts.emplace_back(ctx);
 
             // Save tensors data offset info of the shard.
@@ -933,7 +941,14 @@ bool llama_model_loader::load_all_data(
     // 4 staging buffers for async uploads, each sized 1MB seems to be a good default for single NVMe drives.
     // NVMe raid configurations might require more / larger buffers.
     constexpr size_t n_buffers = 4;
+#if defined(__linux__)
+    constexpr size_t alignment = 4 * 1024; // 4 KiB for Direct I/O
+    // Buffer size: balance between memory usage and I/O efficiency
+    // 64MB works well for NVMe drives
+    constexpr size_t buffer_size = 64 * 1024 * 1024; // 64 MiB
+#else
     constexpr size_t buffer_size = 1 * 1024 * 1024; // 1MB
+#endif
 
     std::vector<ggml_backend_buffer_t> host_buffers;
     std::vector<ggml_backend_event_t> events;
@@ -982,7 +997,11 @@ bool llama_model_loader::load_all_data(
 
         // If the backend is supported, create pinned memory buffers and events for synchronisation.
         for (size_t idx = 0; idx < n_buffers; ++idx) {
+#if defined(__linux__)
+            auto * buf = ggml_backend_buft_alloc_buffer(host_buft, buffer_size + 2 * alignment);
+#else
             auto * buf = ggml_backend_buft_alloc_buffer(host_buft, buffer_size);
+#endif
             if (!buf) {
                 LLAMA_LOG_DEBUG("%s: failed to allocate host buffer for async uploads for device %s\n", func,
                     ggml_backend_dev_name(dev));
@@ -1019,6 +1038,35 @@ bool llama_model_loader::load_all_data(
             ggml_backend_name(upload_backend));
     }
 
+#if defined(__linux__)
+    auto read_aligned_chunk = [](const llama_file * file,
+                                size_t offset,
+                                void * dest,
+                                size_t size,
+                                size_t alignment) {
+        off_t aligned_offset = offset & ~(alignment - 1);
+        off_t offset_from_alignment = offset - aligned_offset;
+        size_t bytes_to_read = (offset_from_alignment + size + alignment - 1) & ~(alignment - 1);
+        
+        void * raw_buffer = nullptr;
+        int ret = posix_memalign(&raw_buffer, alignment, bytes_to_read);
+        if (ret != 0) {
+            throw std::runtime_error(format("posix_memalign failed with error %d", ret));
+        }
+        
+        struct aligned_buffer_deleter {
+            void operator()(void * p) const { free(p); }
+        };
+        std::unique_ptr<void, aligned_buffer_deleter> buffer(raw_buffer);
+        
+        file->seek(aligned_offset, SEEK_SET);
+        file->read_raw(buffer.get(), bytes_to_read);
+        
+        uintptr_t actual_data = reinterpret_cast<uintptr_t>(buffer.get()) + offset_from_alignment;
+        memcpy(dest, reinterpret_cast<void *>(actual_data), size);
+    };
+#endif
+
     for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) {
         const auto * weight = get_weight(ggml_get_name(cur));
         if (weight == nullptr) {
@@ -1064,9 +1112,18 @@ bool llama_model_loader::load_all_data(
             }
         } else {
             const auto & file = files.at(weight->idx);
+#if defined(__linux__)
+            auto offset = (off_t) weight->offs;
+            off_t aligned_offset = offset & ~(alignment - 1);
+            off_t offset_from_alignment = offset - aligned_offset;
+#endif
             if (ggml_backend_buffer_is_host(cur->buffer)) {
+#if defined(__linux__)
+                read_aligned_chunk(file.get(), weight->offs, cur->data, n_size, alignment);
+#else
                 file->seek(weight->offs, SEEK_SET);
                 file->read_raw(cur->data, n_size);
+#endif
                 if (check_tensors) {
                     validation_result.emplace_back(std::async(std::launch::async, [cur, n_size] {
                         return std::make_pair(cur, ggml_validate_row_data(cur->type, cur->data, n_size));
@@ -1075,6 +1132,55 @@ bool llama_model_loader::load_all_data(
             } else {
                 // If upload_backend is valid load the tensor in chunks to pinned memory and upload the buffers asynchronously to the GPU.
                 if (upload_backend) {
+#if defined(__linux__)
+                    // Calculate aligned read boundaries
+                    size_t read_start = aligned_offset;
+                    size_t read_end = (offset + n_size + alignment - 1) & ~(alignment - 1);
+
+                    size_t bytes_read = 0;
+                    size_t data_read = 0;  // Actual tensor data copied (excluding padding)
+
+                    file->seek(aligned_offset, SEEK_SET);
+
+                    while (bytes_read < read_end - read_start) {
+                        size_t read_size = std::min<size_t>(buffer_size, read_end - read_start - bytes_read);
+
+                        // Align the destination pointer within the pinned buffer
+                        uintptr_t ptr_dest_aligned = (reinterpret_cast<uintptr_t>(host_ptrs[buffer_idx]) + alignment - 1) & ~(alignment - 1);
+
+                        // Wait for previous upload to complete before reusing buffer
+                        ggml_backend_event_synchronize(events[buffer_idx]);
+                        
+                        // Read aligned chunk from file
+                        file->read_raw(reinterpret_cast<void *>(ptr_dest_aligned), read_size);
+
+                        // Calculate actual data portion (excluding alignment padding)
+                        uintptr_t ptr_data = ptr_dest_aligned;
+                        size_t data_to_copy = read_size;
+
+                        // Skip alignment padding at start of first chunk
+                        if (bytes_read == 0) {
+                            ptr_data += offset_from_alignment;
+                            data_to_copy -= offset_from_alignment;
+                        }
+                        
+                        // Trim alignment padding at end of last chunk
+                        if (aligned_offset + bytes_read + read_size > offset + n_size) {
+                            data_to_copy -= (read_end - (offset + n_size));
+                        }
+
+                        // Async upload actual data to GPU
+                        ggml_backend_tensor_set_async(upload_backend, cur,
+                                                      reinterpret_cast<void *>(ptr_data), data_read, data_to_copy);
+                        ggml_backend_event_record(events[buffer_idx], upload_backend);
+
+                        data_read += data_to_copy;
+                        bytes_read += read_size;
+
+                        ++buffer_idx;
+                        buffer_idx %= n_buffers;
+                    }
+#else
                     file->seek(weight->offs, SEEK_SET);
 
                     size_t bytes_read = 0;
@@ -1091,10 +1197,15 @@ bool llama_model_loader::load_all_data(
                         ++buffer_idx;
                         buffer_idx %= n_buffers;
                     }
+#endif
                 } else {
                     read_buf.resize(n_size);
+#if defined(__linux__)
+                    read_aligned_chunk(file.get(), weight->offs, read_buf.data(), n_size, alignment);
+#else
                     file->seek(weight->offs, SEEK_SET);
                     file->read_raw(read_buf.data(), n_size);           
+#endif
                     ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size);   
                     if (check_tensors && !ggml_validate_row_data(cur->type, read_buf.data(), n_size)) {
                         throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur)));