OCR filled the GPU: 9 GB for the mobile model on gpu-bench's 30 images, past 16 GB for the server model, which Windows then paged into shared memory (1.5 to 11 s a capture). onnxruntime's logged arena extensions showed why: the arena keeps every block it takes from CUDA and never joins or frees them, and OCR's inputs grow from run to run (recognizer batches went narrowest first; images in any order across calls), so each larger run left another block behind: 5.7 GB of recognizer blocks where the widest batch needs 0.9 GB. - Detection runs the largest images first, recognition the widest batches. - The detector's and recognizer's arenas shrink once each stage of an ocr_process call is done, by a run on a small input with memory.enable_memory_arena_shrinkage. Shrinking after every run cost 33 to 59% of the speed. - The worker threads are made with the engine and kept, not made for every stage of every call (onnxruntime keeps GPU state for each thread that runs a session, microsoft/onnxruntime#33037); 6% faster. - OCR's arenas grow by what is asked (kSameAsRequested). - ort.log_level sets what the engines' onnxruntime environments log; INFO shows every arena extension. Mobile OCR on gpu-bench (RTX 5080 laptop, 4 workers): peak 5.7 GB over the baseline, 2 GB between calls, 142 ms a capture, against 9.0 GB and 126 ms. The text read differs by a line in 2,986 (batches are grouped differently). The server model is not yet measured again.
72 lines
2.6 KiB
Plaintext
72 lines
2.6 KiB
Plaintext
// Type-checks src/App.jai for another OS without producing or linking a binary, to catch errors
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// in code your machine does not build:
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//
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// jai check_targets.jai - linux (from any OS)
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// jai check_targets.jai - macos (from a Mac only: the Objective_C module runs code at compile time)
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//
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// The constants below stand in for the ones first.jai generates; keep their names in step.
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#run,stallable {
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set_build_options_dc(.{do_output = false});
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args := get_build_options().compile_time_command_line;
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target := Operating_System_Tag.LINUX;
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cpu := CPU_Tag.X64;
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if array_find(args, "macos") { target = .MACOS; cpu = .ARM64; }
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options := get_build_options();
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options.os_target = target;
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options.cpu_target = cpu;
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options.output_type = .NO_OUTPUT;
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options.backend = .LLVM;
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options.output_executable_name = "check";
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options.output_path = ".build";
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// rpmalloc (modules/Default_Allocator) also runs at compile time, with the target's OS layer,
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// which crashes on another OS. A check for another OS uses Jai's own allocator instead, through a
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// module of the same name ahead of modules/ (with the thread_idle the app calls, doing nothing).
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if target != OS {
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jai_modules := trim_right(options.import_path[options.import_path.count - 1], "/");
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shim := tprint("%/.build/check_modules", get_working_directory());
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make_directory_if_it_does_not_exist(tprint("%/Default_Allocator", shim), recursive = true);
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write_entire_file(tprint("%/Default_Allocator/module.jai", shim), tprint(#string SHIM
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// Written by check_targets.jai: Jai's own allocator, for type-checking another OS.
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#load "%/Default_Allocator/module.jai";
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thread_idle :: () {}
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SHIM, jai_modules));
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paths: [..] string;
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array_add(*paths, shim);
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array_add(*paths, ..options.import_path);
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options.import_path = paths;
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}
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w := compiler_create_workspace("check");
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set_build_options(options, w);
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compiler_begin_intercept(w);
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add_build_file("src/App.jai", w);
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add_build_string(#string S
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APP_NAME :: "mcap";
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APP_FOLDER_NAME :: "mcap";
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GIT_BRANCH :: "check";
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GIT_HASH :: "-";
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VERSION :: "0.1.24";
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BUILD_DATE :: "-";
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DEBUG :: true;
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COMPILE_TESTS :: true;
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COMPILE_ARGS :: "";
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NO_CRASH_DIALOG :: false;
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S, w);
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while true {
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message := compiler_wait_for_message();
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if message.kind == .COMPLETE {
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m := cast(*Message_Complete) message;
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log("% check: %", target, m.error_code);
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break;
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}
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}
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compiler_end_intercept(w);
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}
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#import "Basic";
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#import "Compiler";
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#import "File";
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#import "String";
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