Merge commit '469c9addef75893e6be12edda852d12e840bf064' into nomic-vulkan

.github/ISSUE_TEMPLATE/bug.md

@@ -1,8 +1,7 @@
 ---
-name: Issue and enhancement template
+name: Bug template
-about: Used to report issues and request enhancements for llama.cpp
+about: Used to report bugs in llama.cpp
-title: "[User] Insert summary of your issue or enhancement.."
+labels: ["bug"]
-labels: ''
 assignees: ''
 
 ---
@@ -46,7 +45,7 @@ $ g++ --version
 
 # Failure Information (for bugs)
 
-Please help provide information about the failure if this is a bug. If it is not a bug, please remove the rest of this template.
+Please help provide information about the failure / bug.
 
 # Steps to Reproduce
 

.github/ISSUE_TEMPLATE/enhancement.md

@@ -0,0 +1,28 @@
+---
+name: Enhancement template
+about: Used to request enhancements for llama.cpp
+labels: ["enhancement"]
+assignees: ''
+
+---
+
+# Prerequisites
+
+Please answer the following questions for yourself before submitting an issue.
+
+- [ ] I am running the latest code. Development is very rapid so there are no tagged versions as of now.
+- [ ] I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md).
+- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed).
+- [ ] I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new bug or useful enhancement to share.
+
+# Feature Description
+
+Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement.
+
+# Motivation
+
+Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users.
+
+# Possible Implementation
+
+If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better.

.github/workflows/build.yml

@@ -276,6 +276,11 @@ jobs:
         run: |
           xcodebuild -scheme llama -destination "${{ matrix.destination }}"
 
+      - name: Build Swift Example
+        id: make_build_swift_example
+        run: |
+            make swift
+
   windows-latest-cmake:
     runs-on: windows-latest
 

.gitignore

@@ -10,6 +10,7 @@
 *.gcno
 *.gcda
 *.dot
+*.bat
 *.metallib
 .DS_Store
 .build/
@@ -44,6 +45,7 @@ models-mnt
 /infill
 /libllama.so
 /llama-bench
+/llava
 /main
 /metal
 /perplexity
@@ -55,6 +57,7 @@ models-mnt
 /server
 /simple
 /batched
+/batched-bench
 /export-lora
 /finetune
 /speculative

CMakeLists.txt

@@ -559,8 +559,7 @@ endif()
 if (LLAMA_ALL_WARNINGS)
     if (NOT MSVC)
         set(warning_flags -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function)
-        set(c_flags -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -Werror=implicit-int
+        set(c_flags -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -Werror=implicit-int -Werror=implicit-function-declaration)
-            -Werror=implicit-function-declaration)
         set(cxx_flags -Wmissing-declarations -Wmissing-noreturn)
         set(host_cxx_flags "")
 
@@ -592,7 +591,8 @@ if (LLAMA_ALL_WARNINGS)
     set(c_flags   ${c_flags}   ${warning_flags})
     set(cxx_flags ${cxx_flags} ${warning_flags})
     add_compile_options("$<$<COMPILE_LANGUAGE:C>:${c_flags}>"
-                        "$<$<COMPILE_LANGUAGE:CXX>:${cxx_flags} ${host_cxx_flags}>")
+                        "$<$<COMPILE_LANGUAGE:CXX>:${cxx_flags}>"
+                        "$<$<COMPILE_LANGUAGE:CXX>:${host_cxx_flags}>")
 
 endif()
 

Makefile

@@ -1,8 +1,14 @@
 # Define the default target now so that it is always the first target
-BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml simple batched save-load-state server embd-input-test gguf llama-bench baby-llama beam-search speculative infill benchmark-matmult parallel finetune export-lora tests/test-c.o
+BUILD_TARGETS = \
+	main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
+	simple batched batched-bench save-load-state server gguf llama-bench llava baby-llama beam-search  \
+	speculative infill benchmark-matmult parallel finetune export-lora tests/test-c.o
 
 # Binaries only useful for tests
-TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe
+TEST_TARGETS = \
+	tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt \
+	tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama          \
+	tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe
 
 # Code coverage output files
 COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report
@@ -172,6 +178,24 @@ else
 	MK_CPPFLAGS += -DNDEBUG
 endif
 
+ifdef LLAMA_SANITIZE_THREAD
+	MK_CFLAGS   += -fsanitize=thread -g
+	MK_CXXFLAGS += -fsanitize=thread -g
+	MK_LDFLAGS  += -fsanitize=thread -g
+endif
+
+ifdef LLAMA_SANITIZE_ADDRESS
+	MK_CFLAGS   += -fsanitize=address -fno-omit-frame-pointer -g
+	MK_CXXFLAGS += -fsanitize=address -fno-omit-frame-pointer -g
+	MK_LDFLAGS  += -fsanitize=address -fno-omit-frame-pointer -g
+endif
+
+ifdef LLAMA_SANITIZE_UNDEFINED
+	MK_CFLAGS   += -fsanitize=undefined -g
+	MK_CXXFLAGS += -fsanitize=undefined -g
+	MK_LDFLAGS  += -fsanitize=undefined -g
+endif
+
 ifdef LLAMA_SERVER_VERBOSE
 	MK_CPPFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
 endif
@@ -520,7 +544,13 @@ OBJS += ggml-alloc.o ggml-backend.o
 llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-common.o: common/common.cpp common/common.h build-info.h common/log.h
+COMMON_H_DEPS = common/common.h common/sampling.h build-info.h common/log.h
+COMMON_DEPS   = $(COMMON_H_DEPS) common.o sampling.o grammar-parser.o
+
+common.o: common/common.cpp $(COMMON_H_DEPS)
+	$(CXX) $(CXXFLAGS) -c $< -o $@
+
+sampling.o: common/sampling.cpp $(COMMON_H_DEPS)
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
 console.o: common/console.cpp common/console.h
@@ -542,19 +572,22 @@ clean:
 # Examples
 #
 
-main: examples/main/main.cpp                                  build-info.h ggml.o llama.o common.o console.o grammar-parser.o $(OBJS)
+main: examples/main/main.cpp                                  build-info.h ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 	@echo
 	@echo '====  Run ./main -h for help.  ===='
 	@echo
 
-infill: examples/infill/infill.cpp                            build-info.h ggml.o llama.o common.o console.o grammar-parser.o $(OBJS)
+infill: examples/infill/infill.cpp                            build-info.h ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-simple: examples/simple/simple.cpp                            build-info.h ggml.o llama.o common.o $(OBJS)
+simple: examples/simple/simple.cpp                            build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-batched: examples/batched/batched.cpp                         build-info.h ggml.o llama.o common.o $(OBJS)
+batched: examples/batched/batched.cpp                         build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+
+batched-bench: examples/batched-bench/batched-bench.cpp       build-info.h ggml.o llama.o common.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 quantize: examples/quantize/quantize.cpp                      build-info.h ggml.o llama.o $(OBJS)
@@ -563,53 +596,49 @@ quantize: examples/quantize/quantize.cpp                      build-info.h ggml.
 quantize-stats: examples/quantize-stats/quantize-stats.cpp    build-info.h ggml.o llama.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-perplexity: examples/perplexity/perplexity.cpp                build-info.h ggml.o llama.o common.o $(OBJS)
+perplexity: examples/perplexity/perplexity.cpp                build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-embedding: examples/embedding/embedding.cpp                   build-info.h ggml.o llama.o common.o $(OBJS)
+embedding: examples/embedding/embedding.cpp                   build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS)
+server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
-	$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2)
+	$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) -Wno-cast-qual
-
-$(LIB_PRE)embdinput$(DSO_EXT): examples/embd-input/embd-input.h examples/embd-input/embd-input-lib.cpp build-info.h ggml.o llama.o common.o $(OBJS)
-	$(CXX) --shared $(CXXFLAGS) $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS)
-
-
-embd-input-test: $(LIB_PRE)embdinput$(DSO_EXT) examples/embd-input/embd-input-test.cpp build-info.h ggml.o llama.o common.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %$(DSO_EXT),$(filter-out %.h,$(filter-out %.hpp,$^))) -o $@ $(LDFLAGS) -L. -lembdinput
 
 gguf: examples/gguf/gguf.cpp ggml.o llama.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o common.o train.o $(OBJS)
+train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-llama-bench: examples/llama-bench/llama-bench.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+llama-bench: examples/llama-bench/llama-bench.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o common.o train.o $(OBJS)
+llava: examples/llava/llava.cpp examples/llava/llava-utils.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -Wno-cast-qual
+
+baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-beam-search: examples/beam-search/beam-search.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+beam-search: examples/beam-search/beam-search.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-finetune: examples/finetune/finetune.cpp build-info.h ggml.o llama.o common.o train.o $(OBJS)
+finetune: examples/finetune/finetune.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-export-lora: examples/export-lora/export-lora.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+export-lora: examples/export-lora/export-lora.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS)
+speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-parallel: examples/parallel/parallel.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+parallel: examples/parallel/parallel.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 ifdef LLAMA_METAL
@@ -617,6 +646,11 @@ metal: examples/metal/metal.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
 endif
 
+ifeq ($(UNAME_S),Darwin)
+swift: examples/batched.swift
+	(cd examples/batched.swift; make build)
+endif
+
 build-info.h: $(wildcard .git/index) scripts/build-info.sh
 	@sh scripts/build-info.sh $(CC) > $@.tmp
 	@if ! cmp -s $@.tmp $@; then \
@@ -637,7 +671,7 @@ benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.h ggml.o
 run-benchmark-matmult: benchmark-matmult
 	./$@
 
-.PHONY: run-benchmark-matmult
+.PHONY: run-benchmark-matmult swift
 
 vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
@@ -645,40 +679,40 @@ vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
 q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
 
-tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o common.o grammar-parser.o $(OBJS)
+tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-grammar-parser: tests/test-grammar-parser.cpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS)
+tests/test-grammar-parser: tests/test-grammar-parser.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-double-float: tests/test-double-float.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+tests/test-double-float: tests/test-double-float.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-grad0: tests/test-grad0.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+tests/test-grad0: tests/test-grad0.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-opt: tests/test-opt.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+tests/test-opt: tests/test-opt.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-quantize-fns: tests/test-quantize-fns.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+tests/test-quantize-fns: tests/test-quantize-fns.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-quantize-perf: tests/test-quantize-perf.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+tests/test-quantize-perf: tests/test-quantize-perf.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-sampling: tests/test-sampling.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+tests/test-sampling: tests/test-sampling.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp build-info.h ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 tests/test-c.o: tests/test-c.c llama.h

Package.swift

@@ -1,10 +1,10 @@
-// swift-tools-version:5.3
+// swift-tools-version:5.5
 
 import PackageDescription
 
 #if arch(arm) || arch(arm64)
 let platforms: [SupportedPlatform]? = [
-    .macOS(.v11),
+    .macOS(.v12),
     .iOS(.v14),
     .watchOS(.v4),
     .tvOS(.v14)
@@ -41,12 +41,13 @@ let package = Package(
                 "ggml.c",
                 "llama.cpp",
                 "ggml-alloc.c",
+                "ggml-backend.c",
                 "k_quants.c",
             ] + additionalSources,
             resources: resources,
             publicHeadersPath: "spm-headers",
             cSettings: [
-                .unsafeFlags(["-Wno-shorten-64-to-32"]),
+                .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]),
                 .define("GGML_USE_K_QUANTS"),
                 .define("GGML_USE_ACCELERATE")
                 // NOTE: NEW_LAPACK will required iOS version 16.4+

README.md

@@ -11,12 +11,8 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 
 ### Hot topics
 
-- ‼️ Breaking change: `rope_freq_base` and `rope_freq_scale` must be set to zero to use the model default values: [#3401](https://github.com/ggerganov/llama.cpp/pull/3401)
+- LLaVA support: https://github.com/ggerganov/llama.cpp/pull/3436
-- Parallel decoding + continuous batching support added: [#3228](https://github.com/ggerganov/llama.cpp/pull/3228) \
+- ‼️ BPE tokenizer update: existing Falcon and Starcoder `.gguf` models will need to be reconverted: [#3252](https://github.com/ggerganov/llama.cpp/pull/3252)
-  **Devs should become familiar with the new API**
-- Local Falcon 180B inference on Mac Studio
-
-  https://github.com/ggerganov/llama.cpp/assets/1991296/98abd4e8-7077-464c-ae89-aebabca7757e
 
 ----
 
@@ -89,19 +85,23 @@ as the main playground for developing new features for the [ggml](https://github
 - [X] [Vicuna](https://github.com/ggerganov/llama.cpp/discussions/643#discussioncomment-5533894)
 - [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
 - [X] [OpenBuddy 🐶 (Multilingual)](https://github.com/OpenBuddy/OpenBuddy)
-- [X] [Pygmalion 7B / Metharme 7B](#using-pygmalion-7b--metharme-7b)
+- [X] [Pygmalion/Metharme](#using-pygmalion-7b--metharme-7b)
 - [X] [WizardLM](https://github.com/nlpxucan/WizardLM)
-- [X] [Baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B) and its derivations (such as [baichuan-7b-sft](https://huggingface.co/hiyouga/baichuan-7b-sft))
+- [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft)
-- [X] [Aquila-7B](https://huggingface.co/BAAI/Aquila-7B) / [AquilaChat-7B](https://huggingface.co/BAAI/AquilaChat-7B)
+- [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila)
 - [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187)
 - [X] [Mistral AI v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
 - [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim)
+- [X] [Persimmon 8B](https://github.com/ggerganov/llama.cpp/pull/3410)
+- [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417)
+- [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553)
+
 
 **Bindings:**
 
 - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
 - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
-- Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp), [hlhr202/llama-node](https://github.com/hlhr202/llama-node)
+- Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp)
 - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
 - Rust: [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
 - C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
@@ -204,7 +204,7 @@ https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8
 
 ## Usage
 
-Here are the steps for the LLaMA-7B model.
+Here are the end-to-end binary build and model conversion steps for the LLaMA-7B model.
 
 ### Get the Code
 
@@ -277,7 +277,7 @@ In order to build llama.cpp you have three different options.
 On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
 To disable the Metal build at compile time use the `LLAMA_NO_METAL=1` flag or the `LLAMA_METAL=OFF` cmake option.
 
-When built with Metal support, you can explicitly disable GPU inference with the `--gpu-layers|-ngl 0` command-line
+When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers|-ngl 0` command-line
 argument.
 
 ### MPI Build
@@ -571,6 +571,18 @@ python3 convert.py models/7B/
 
 When running the larger models, make sure you have enough disk space to store all the intermediate files.
 
+### Running on Windows with prebuilt binaries
+
+You will find prebuilt Windows binaries on the release page.
+
+Simply download and extract the latest zip package of choice: (e.g. `llama-b1380-bin-win-avx2-x64.zip`)
+
+From the unzipped folder, open a terminal/cmd window here and place a pre-converted `.gguf` model file. Test out the main example like so:
+
+```
+.\main -m llama-2-7b.Q4_0.gguf -n 128
+```
+
 ### Memory/Disk Requirements
 
 As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same.
@@ -950,7 +962,6 @@ docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /
 
 - [main](./examples/main/README.md)
 - [server](./examples/server/README.md)
-- [embd-input](./examples/embd-input/README.md)
 - [jeopardy](./examples/jeopardy/README.md)
 - [BLIS](./docs/BLIS.md)
 - [Performance troubleshooting](./docs/token_generation_performance_tips.md)

build.zig

@@ -128,17 +128,19 @@ pub fn build(b: *std.build.Builder) !void {
     const llama = make.obj("llama", "llama.cpp");
     const common = make.obj("common", "common/common.cpp");
     const console = make.obj("console", "common/console.cpp");
+    const sampling = make.obj("sampling", "common/sampling.cpp");
     const grammar_parser = make.obj("grammar-parser", "common/grammar-parser.cpp");
     const train = make.obj("train", "common/train.cpp");
+    const clip = make.obj("clip", "examples/llava/clip.cpp");
 
-    _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, console, grammar_parser });
+    _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, sampling, console, grammar_parser });
     _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common });
     _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common });
     _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common });
     _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, train });
     _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, train });
 
-    const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, grammar_parser });
+    const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, llama, common, sampling, grammar_parser, clip });
     if (server.target.isWindows()) {
         server.linkSystemLibrary("ws2_32");
     }

ci/run.sh

@@ -208,6 +208,8 @@ function gg_run_open_llama_3b_v2 {
     (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
     (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
+    (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+
     function check_ppl {
         qnt="$1"
         ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
@@ -296,6 +298,7 @@ function gg_sum_open_llama_3b_v2 {
     gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
     gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
     gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
+    gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
     gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)"
     gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)"
     gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)"
@@ -382,6 +385,8 @@ function gg_run_open_llama_7b_v2 {
     (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
     (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
+    (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+
     function check_ppl {
         qnt="$1"
         ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
@@ -470,6 +475,7 @@ function gg_sum_open_llama_7b_v2 {
     gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
     gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
     gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
+    gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
     gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)"
     gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)"
     #gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)"
@@ -496,10 +502,12 @@ test $ret -eq 0 && gg_run ctest_debug
 test $ret -eq 0 && gg_run ctest_release
 
 if [ -z ${GG_BUILD_LOW_PERF} ]; then
-    if [ -z ${GG_BUILD_CUDA} ]; then
+    if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then
-        test $ret -eq 0 && gg_run open_llama_3b_v2
+        if [ -z ${GG_BUILD_CUDA} ]; then
-    else
+            test $ret -eq 0 && gg_run open_llama_3b_v2
-        test $ret -eq 0 && gg_run open_llama_7b_v2
+        else
+            test $ret -eq 0 && gg_run open_llama_7b_v2
+        fi
     fi
 fi
 

common/CMakeLists.txt

@@ -5,6 +5,8 @@ set(TARGET common)
 add_library(${TARGET} OBJECT
     common.h
     common.cpp
+    sampling.h
+    sampling.cpp
     console.h
     console.cpp
     grammar-parser.h

common/common.cpp

@@ -107,6 +107,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
     std::string arg;
     gpt_params default_params;
     const std::string arg_prefix = "--";
+    llama_sampling_params & sparams = params.sparams;
 
     for (int i = 1; i < argc; i++) {
         arg = argv[i];
@@ -184,7 +185,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 invalid_param = true;
                 break;
             }
-            params.top_k = std::stoi(argv[i]);
+            sparams.top_k = std::stoi(argv[i]);
         } else if (arg == "-c" || arg == "--ctx-size") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -216,73 +217,74 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 invalid_param = true;
                 break;
             }
-            params.top_p = std::stof(argv[i]);
+            sparams.top_p = std::stof(argv[i]);
         } else if (arg == "--temp") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params.temp = std::stof(argv[i]);
+            sparams.temp = std::stof(argv[i]);
         } else if (arg == "--tfs") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params.tfs_z = std::stof(argv[i]);
+            sparams.tfs_z = std::stof(argv[i]);
         } else if (arg == "--typical") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params.typical_p = std::stof(argv[i]);
+            sparams.typical_p = std::stof(argv[i]);
         } else if (arg == "--repeat-last-n") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params.repeat_last_n = std::stoi(argv[i]);
+            sparams.penalty_last_n = std::stoi(argv[i]);
+            sparams.n_prev = std::max(sparams.n_prev, sparams.penalty_last_n);
         } else if (arg == "--repeat-penalty") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params.repeat_penalty = std::stof(argv[i]);
+            sparams.penalty_repeat = std::stof(argv[i]);
         } else if (arg == "--frequency-penalty") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params.frequency_penalty = std::stof(argv[i]);
+            sparams.penalty_freq = std::stof(argv[i]);
         } else if (arg == "--presence-penalty") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params.presence_penalty = std::stof(argv[i]);
+            sparams.penalty_present = std::stof(argv[i]);
         } else if (arg == "--mirostat") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params.mirostat = std::stoi(argv[i]);
+            sparams.mirostat = std::stoi(argv[i]);
         } else if (arg == "--mirostat-lr") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params.mirostat_eta = std::stof(argv[i]);
+            sparams.mirostat_eta = std::stof(argv[i]);
         } else if (arg == "--mirostat-ent") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params.mirostat_tau = std::stof(argv[i]);
+            sparams.mirostat_tau = std::stof(argv[i]);
         } else if (arg == "--cfg-negative-prompt") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params.cfg_negative_prompt = argv[i];
+            sparams.cfg_negative_prompt = argv[i];
         } else if (arg == "--cfg-negative-prompt-file") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -294,16 +296,16 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 invalid_param = true;
                 break;
             }
-            std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.cfg_negative_prompt));
+            std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(sparams.cfg_negative_prompt));
-            if (!params.cfg_negative_prompt.empty() && params.cfg_negative_prompt.back() == '\n') {
+            if (!sparams.cfg_negative_prompt.empty() && sparams.cfg_negative_prompt.back() == '\n') {
-                params.cfg_negative_prompt.pop_back();
+                sparams.cfg_negative_prompt.pop_back();
             }
         } else if (arg == "--cfg-scale") {
             if (++i >= argc) {
                 invalid_param = true;
                 break;
             }
-            params.cfg_scale = std::stof(argv[i]);
+            sparams.cfg_scale = std::stof(argv[i]);
         } else if (arg == "-b" || arg == "--batch-size") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -383,6 +385,18 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.lora_base = argv[i];
+        } else if (arg == "--mmproj") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.mmproj = argv[i];
+        } else if (arg == "--image") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.image = argv[i];
         } else if (arg == "-i" || arg == "--interactive") {
             params.interactive = true;
         } else if (arg == "--embedding") {
@@ -512,7 +526,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
         } else if (arg == "--ignore-eos") {
             params.ignore_eos = true;
         } else if (arg == "--no-penalize-nl") {
-            params.penalize_nl = false;
+            sparams.penalize_nl = false;
         } else if (arg == "-l" || arg == "--logit-bias") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -524,7 +538,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
             std::string value_str;
             try {
                 if (ss >> key && ss >> sign && std::getline(ss, value_str) && (sign == '+' || sign == '-')) {
-                    params.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
+                    sparams.logit_bias[key] = std::stof(value_str) * ((sign == '-') ? -1.0f : 1.0f);
                 } else {
                     throw std::exception();
                 }
@@ -559,7 +573,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 invalid_param = true;
                 break;
             }
-            params.grammar = argv[i];
+            sparams.grammar = argv[i];
         } else if (arg == "--grammar-file") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -574,7 +588,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
             std::copy(
                 std::istreambuf_iterator<char>(file),
                 std::istreambuf_iterator<char>(),
-                std::back_inserter(params.grammar)
+                std::back_inserter(sparams.grammar)
             );
 #ifndef LOG_DISABLE_LOGS
         // Parse args for logging parameters
@@ -618,6 +632,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
         process_escapes(params.prompt);
         process_escapes(params.input_prefix);
         process_escapes(params.input_suffix);
+        process_escapes(sparams.cfg_negative_prompt);
         for (auto & antiprompt : params.antiprompt) {
             process_escapes(antiprompt);
         }
@@ -627,6 +642,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
 }
 
 void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
+    const llama_sampling_params & sparams = params.sparams;
+
     printf("usage: %s [options]\n", argv[0]);
     printf("\n");
     printf("options:\n");
@@ -659,19 +676,19 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  -n N, --n-predict N   number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
     printf("  -c N, --ctx-size N    size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx);
     printf("  -b N, --batch-size N  batch size for prompt processing (default: %d)\n", params.n_batch);
-    printf("  --top-k N             top-k sampling (default: %d, 0 = disabled)\n", params.top_k);
+    printf("  --top-k N             top-k sampling (default: %d, 0 = disabled)\n", sparams.top_k);
-    printf("  --top-p N             top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p);
+    printf("  --top-p N             top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)sparams.top_p);
-    printf("  --tfs N               tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z);
+    printf("  --tfs N               tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)sparams.tfs_z);
-    printf("  --typical N           locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)params.typical_p);
+    printf("  --typical N           locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)sparams.typical_p);
-    printf("  --repeat-last-n N     last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", params.repeat_last_n);
+    printf("  --repeat-last-n N     last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", sparams.penalty_last_n);
-    printf("  --repeat-penalty N    penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)params.repeat_penalty);
+    printf("  --repeat-penalty N    penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)sparams.penalty_repeat);
-    printf("  --presence-penalty N  repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)params.presence_penalty);
+    printf("  --presence-penalty N  repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_present);
-    printf("  --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)params.frequency_penalty);
+    printf("  --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_freq);
     printf("  --mirostat N          use Mirostat sampling.\n");
     printf("                        Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n");
-    printf("                        (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", params.mirostat);
+    printf("                        (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", sparams.mirostat);
-    printf("  --mirostat-lr N       Mirostat learning rate, parameter eta (default: %.1f)\n", (double)params.mirostat_eta);
+    printf("  --mirostat-lr N       Mirostat learning rate, parameter eta (default: %.1f)\n", (double)sparams.mirostat_eta);
-    printf("  --mirostat-ent N      Mirostat target entropy, parameter tau (default: %.1f)\n", (double)params.mirostat_tau);
+    printf("  --mirostat-ent N      Mirostat target entropy, parameter tau (default: %.1f)\n", (double)sparams.mirostat_tau);
     printf("  -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n");
     printf("                        modifies the likelihood of token appearing in the completion,\n");
     printf("                        i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n");
@@ -682,7 +699,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("                        negative prompt to use for guidance. (default: empty)\n");
     printf("  --cfg-negative-prompt-file FNAME\n");
     printf("                        negative prompt file to use for guidance. (default: empty)\n");
-    printf("  --cfg-scale N         strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale);
+    printf("  --cfg-scale N         strength of guidance (default: %f, 1.0 = disable)\n", sparams.cfg_scale);
     printf("  --rope-scale N        RoPE context linear scaling factor, inverse of --rope-freq-scale\n");
     printf("  --rope-freq-base N    RoPE base frequency, used by NTK-aware scaling (default: loaded from model)\n");
     printf("  --rope-freq-scale N   RoPE frequency linear scaling factor (default: loaded from model)\n");
@@ -690,7 +707,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  --no-penalize-nl      do not penalize newline token\n");
     printf("  --memory-f32          use f32 instead of f16 for memory key+value (default: disabled)\n");
     printf("                        not recommended: doubles context memory required and no measurable increase in quality\n");
-    printf("  --temp N              temperature (default: %.1f)\n", (double)params.temp);
+    printf("  --temp N              temperature (default: %.1f)\n", (double)sparams.temp);
     printf("  --logits-all          return logits for all tokens in the batch (default: disabled)\n");
     printf("  --hellaswag           compute HellaSwag score over random tasks from datafile supplied with -f\n");
     printf("  --hellaswag-tasks N   number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks);
@@ -700,6 +717,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     printf("  -np N, --parallel N   number of parallel sequences to decode (default: %d)\n", params.n_parallel);
     printf("  -ns N, --sequences N  number of sequences to decode (default: %d)\n", params.n_sequences);
     printf("  -cb, --cont-batching  enable continuous batching (a.k.a dynamic batching) (default: disabled)\n");
+    printf("  --mmproj MMPROJ_FILE  path to a multimodal projector file for LLaVA. see examples/llava/README.md\n");
+    printf("  --image IMAGE_FILE    path to an image file. use with multimodal models\n");
     if (llama_mlock_supported()) {
         printf("  --mlock               force system to keep model in RAM rather than swapping or compressing\n");
     }
@@ -803,6 +822,27 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     return cparams;
 }
 
+void llama_batch_clear(struct llama_batch & batch) {
+    batch.n_tokens = 0;
+}
+
+void llama_batch_add(
+                 struct llama_batch & batch,
+                        llama_token   id,
+                          llama_pos   pos,
+    const std::vector<llama_seq_id> & seq_ids,
+                               bool   logits) {
+    batch.token   [batch.n_tokens] = id;
+    batch.pos     [batch.n_tokens] = pos,
+    batch.n_seq_id[batch.n_tokens] = seq_ids.size();
+    for (size_t i = 0; i < seq_ids.size(); ++i) {
+        batch.seq_id[batch.n_tokens][i] = seq_ids[i];
+    }
+    batch.logits  [batch.n_tokens] = logits;
+
+    batch.n_tokens++;
+}
+
 std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(gpt_params & params) {
     auto mparams = llama_model_params_from_gpt_params(params);
 
@@ -840,13 +880,13 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
     }
 
     if (params.ignore_eos) {
-        params.logit_bias[llama_token_eos(lctx)] = -INFINITY;
+        params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY;
     }
 
     {
         LOG("warming up the model with an empty run\n");
 
-        std::vector<llama_token> tmp = { llama_token_bos(lctx), llama_token_eos(lctx), };
+        std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), };
         llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
         llama_kv_cache_tokens_rm(lctx, -1, -1);
         llama_reset_timings(lctx);
@@ -862,21 +902,23 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
 std::vector<llama_token> llama_tokenize(
   const struct llama_context * ctx,
            const std::string & text,
-                        bool   add_bos) {
+                        bool   add_bos,
-    return llama_tokenize(llama_get_model(ctx), text, add_bos);
+                        bool   special) {
+    return llama_tokenize(llama_get_model(ctx), text, add_bos, special);
 }
 
 std::vector<llama_token> llama_tokenize(
     const struct llama_model * model,
            const std::string & text,
-                        bool   add_bos) {
+                        bool   add_bos,
+                        bool   special) {
     // upper limit for the number of tokens
     int n_tokens = text.length() + add_bos;
     std::vector<llama_token> result(n_tokens);
-    n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos);
+    n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, special);
     if (n_tokens < 0) {
         result.resize(-n_tokens);
-        int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos);
+        int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, special);
         GGML_ASSERT(check == -n_tokens);
     } else {
         result.resize(n_tokens);
@@ -899,7 +941,7 @@ std::string llama_token_to_piece(const struct llama_context * ctx, llama_token t
 }
 
 std::string llama_detokenize_spm(llama_context * ctx, const std::vector<llama_token> & tokens) {
-    const llama_token bos_id = llama_token_bos(ctx);
+    const llama_token bos_id = llama_token_bos(llama_get_model(ctx));
 
     std::string piece;
     std::string result;
@@ -932,127 +974,6 @@ std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_to
     return result;
 }
 
-//
-// Sampling utils
-//
-
-llama_token llama_sample_token(
-                  struct llama_context * ctx,
-                  struct llama_context * ctx_guidance,
-                  struct llama_grammar * grammar,
-               const struct gpt_params & params,
-        const std::vector<llama_token> & last_tokens,
-         std::vector<llama_token_data> & candidates,
-                                   int   idx) {
-    const int n_ctx   = llama_n_ctx(ctx);
-    const int n_vocab = llama_n_vocab(llama_get_model(ctx));
-
-    const float   temp            = params.temp;
-    const int32_t top_k           = params.top_k <= 0 ? n_vocab : params.top_k;
-    const float   top_p           = params.top_p;
-    const float   tfs_z           = params.tfs_z;
-    const float   typical_p       = params.typical_p;
-    const int32_t repeat_last_n   = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
-    const float   repeat_penalty  = params.repeat_penalty;
-    const float   alpha_presence  = params.presence_penalty;
-    const float   alpha_frequency = params.frequency_penalty;
-    const int     mirostat        = params.mirostat;
-    const float   mirostat_tau    = params.mirostat_tau;
-    const float   mirostat_eta    = params.mirostat_eta;
-    const bool    penalize_nl     = params.penalize_nl;
-
-    llama_token id = 0;
-
-    float * logits = llama_get_logits_ith(ctx, idx);
-
-    // Apply params.logit_bias map
-    for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
-        logits[it->first] += it->second;
-    }
-
-    candidates.clear();
-    for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-        candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
-    }
-
-    llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
-
-    if (ctx_guidance) {
-        llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale);
-    }
-
-    // apply penalties
-    if (!last_tokens.empty()) {
-        const float nl_logit = logits[llama_token_nl(ctx)];
-        const int last_n_repeat = std::min(std::min((int)last_tokens.size(), repeat_last_n), n_ctx);
-
-        llama_sample_repetition_penalty(ctx, &cur_p,
-                last_tokens.data() + last_tokens.size() - last_n_repeat,
-                last_n_repeat, repeat_penalty);
-        llama_sample_frequency_and_presence_penalties(ctx, &cur_p,
-                last_tokens.data() + last_tokens.size() - last_n_repeat,
-                last_n_repeat, alpha_frequency, alpha_presence);
-
-        if (!penalize_nl) {
-            for (size_t idx = 0; idx < cur_p.size; idx++) {
-                if (cur_p.data[idx].id == llama_token_nl(ctx)) {
-                    cur_p.data[idx].logit = nl_logit;
-                    break;
-                }
-            }
-        }
-    }
-
-    if (grammar != NULL) {
-        llama_sample_grammar(ctx, &cur_p, grammar);
-    }
-
-    if (temp <= 0) {
-        // Greedy sampling
-        id = llama_sample_token_greedy(ctx, &cur_p);
-    } else {
-        if (mirostat == 1) {
-            static float mirostat_mu = 2.0f * mirostat_tau;
-            const int mirostat_m = 100;
-            llama_sample_temp(ctx, &cur_p, temp);
-            id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
-        } else if (mirostat == 2) {
-            static float mirostat_mu = 2.0f * mirostat_tau;
-            llama_sample_temp(ctx, &cur_p, temp);
-            id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu);
-        } else {
-            // Temperature sampling
-            size_t min_keep = std::max(1, params.n_probs);
-            llama_sample_top_k      (ctx, &cur_p, top_k, min_keep);
-            llama_sample_tail_free  (ctx, &cur_p, tfs_z, min_keep);
-            llama_sample_typical    (ctx, &cur_p, typical_p, min_keep);
-            llama_sample_top_p      (ctx, &cur_p, top_p, min_keep);
-            llama_sample_temp(ctx, &cur_p, temp);
-
-            {
-                const int n_top = 10;
-                LOG("top %d candidates:\n", n_top);
-
-                for (int i = 0; i < n_top; i++) {
-                    const llama_token id = cur_p.data[i].id;
-                    LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p);
-                }
-            }
-
-            id = llama_sample_token(ctx, &cur_p);
-
-            LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str());
-        }
-    }
-    // printf("`%d`", candidates_p.size);
-
-    if (grammar != NULL) {
-        llama_grammar_accept_token(ctx, grammar, id);
-    }
-
-    return id;
-}
-
 //
 // YAML utils
 //
@@ -1204,26 +1125,28 @@ std::string get_sortable_timestamp() {
 
 void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const llama_context * lctx,
                                const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
+    const llama_sampling_params & sparams = params.sparams;
+
     fprintf(stream, "build_commit: %s\n", BUILD_COMMIT);
     fprintf(stream, "build_number: %d\n", BUILD_NUMBER);
-    fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false");
+    fprintf(stream, "cpu_has_arm_fma: %s\n",     ggml_cpu_has_arm_fma()     ? "true" : "false");
-    fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false");
+    fprintf(stream, "cpu_has_avx: %s\n",         ggml_cpu_has_avx()         ? "true" : "false");
-    fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false");
+    fprintf(stream, "cpu_has_avx2: %s\n",        ggml_cpu_has_avx2()        ? "true" : "false");
-    fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false");
+    fprintf(stream, "cpu_has_avx512: %s\n",      ggml_cpu_has_avx512()      ? "true" : "false");
     fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false");
     fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false");
-    fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
+    fprintf(stream, "cpu_has_blas: %s\n",        ggml_cpu_has_blas()        ? "true" : "false");
-    fprintf(stream, "cpu_has_cublas: %s\n", ggml_cpu_has_cublas() ? "true" : "false");
+    fprintf(stream, "cpu_has_cublas: %s\n",      ggml_cpu_has_cublas()      ? "true" : "false");
-    fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false");
+    fprintf(stream, "cpu_has_clblast: %s\n",     ggml_cpu_has_clblast()     ? "true" : "false");
-    fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false");
+    fprintf(stream, "cpu_has_fma: %s\n",         ggml_cpu_has_fma()         ? "true" : "false");
-    fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false");
+    fprintf(stream, "cpu_has_gpublas: %s\n",     ggml_cpu_has_gpublas()     ? "true" : "false");
-    fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false");
+    fprintf(stream, "cpu_has_neon: %s\n",        ggml_cpu_has_neon()        ? "true" : "false");
-    fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false");
+    fprintf(stream, "cpu_has_f16c: %s\n",        ggml_cpu_has_f16c()        ? "true" : "false");
-    fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false");
+    fprintf(stream, "cpu_has_fp16_va: %s\n",     ggml_cpu_has_fp16_va()     ? "true" : "false");
-    fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false");
+    fprintf(stream, "cpu_has_wasm_simd: %s\n",   ggml_cpu_has_wasm_simd()   ? "true" : "false");
-    fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
+    fprintf(stream, "cpu_has_blas: %s\n",        ggml_cpu_has_blas()        ? "true" : "false");
-    fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false");
+    fprintf(stream, "cpu_has_sse3: %s\n",        ggml_cpu_has_sse3()        ? "true" : "false");
-    fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false");
+    fprintf(stream, "cpu_has_vsx: %s\n",         ggml_cpu_has_vsx()         ? "true" : "false");
 
 #ifdef NDEBUG
     fprintf(stream, "debug: false\n");
@@ -1250,21 +1173,21 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
 
     fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str());
     fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch);
-    dump_string_yaml_multiline(stream, "cfg_negative_prompt", params.cfg_negative_prompt.c_str());
+    dump_string_yaml_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str());
-    fprintf(stream, "cfg_scale: %f # default: 1.0\n", params.cfg_scale);
+    fprintf(stream, "cfg_scale: %f # default: 1.0\n", sparams.cfg_scale);
     fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
     fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
     fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
     fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
     fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
-    fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", params.frequency_penalty);
+    fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq);
-    dump_string_yaml_multiline(stream, "grammar", params.grammar.c_str());
+    dump_string_yaml_multiline(stream, "grammar", sparams.grammar.c_str());
     fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n");
     fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false");
     fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks);
 
-    const auto logit_bias_eos = params.logit_bias.find(llama_token_eos(lctx));
+    const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx)));
-    const bool ignore_eos = logit_bias_eos != params.logit_bias.end() && logit_bias_eos->second == -INFINITY;
+    const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY;
     fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false");
 
     dump_string_yaml_multiline(stream, "in_prefix", params.input_prefix.c_str());
@@ -1277,7 +1200,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str());
 
     fprintf(stream, "logit_bias:\n");
-    for (std::pair<llama_token, float> lb : params.logit_bias) {
+    for (std::pair<llama_token, float> lb : sparams.logit_bias) {
         if (ignore_eos && lb.first == logit_bias_eos->first) {
             continue;
         }
@@ -1301,30 +1224,30 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "lora_base: %s\n", params.lora_base.c_str());
     fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
     fprintf(stream, "memory_f32: %s # default: false\n", !params.memory_f16 ? "true" : "false");
-    fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", params.mirostat);
+    fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat);
-    fprintf(stream, "mirostat_ent: %f # default: 5.0\n", params.mirostat_tau);
+    fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau);
-    fprintf(stream, "mirostat_lr: %f # default: 0.1\n", params.mirostat_eta);
+    fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta);
     fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false");
     fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str());
     fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());
     fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
     fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
     fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
-    fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", params.n_probs);
+    fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs);
     fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
     fprintf(stream, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false");
-    fprintf(stream, "no_penalize_nl: %s # default: false\n", !params.penalize_nl ? "true" : "false");
+    fprintf(stream, "no_penalize_nl: %s # default: false\n", !sparams.penalize_nl ? "true" : "false");
     fprintf(stream, "numa: %s # default: false\n", params.numa ? "true" : "false");
     fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
     fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
-    fprintf(stream, "presence_penalty: %f # default: 0.0\n", params.presence_penalty);
+    fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present);
     dump_string_yaml_multiline(stream, "prompt", params.prompt.c_str());
     fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str());
     fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false");
     fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false");
     dump_vector_int_yaml(stream, "prompt_tokens", prompt_tokens);
     fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false");
-    fprintf(stream, "repeat_penalty: %f # default: 1.1\n", params.repeat_penalty);
+    fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat);
 
     fprintf(stream, "reverse_prompt:\n");
     for (std::string ap : params.antiprompt) {
@@ -1342,15 +1265,15 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
     fprintf(stream, "seed: %d # default: -1 (random seed)\n", params.seed);
     fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
     fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false");
-    fprintf(stream, "temp: %f # default: 0.8\n", params.temp);
+    fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp);
 
     const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + LLAMA_MAX_DEVICES);
     dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector);
 
-    fprintf(stream, "tfs: %f # default: 1.0\n", params.tfs_z);
+    fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z);
     fprintf(stream, "threads: %d # default: %d\n", params.n_threads, std::thread::hardware_concurrency());
-    fprintf(stream, "top_k: %d # default: 40\n", params.top_k);
+    fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k);
-    fprintf(stream, "top_p: %f # default: 0.95\n", params.top_p);
+    fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p);
-    fprintf(stream, "typical_p: %f # default: 1.0\n", params.typical_p);
+    fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p);
     fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
 }

common/common.h

@@ -4,6 +4,8 @@
 
 #include "llama.h"
 
+#include "sampling.h"
+
 #define LOG_NO_FILE_LINE_FUNCTION
 #include "log.h"
 
@@ -49,31 +51,12 @@ struct gpt_params {
     int32_t n_gpu_layers_draft              = -1;   // number of layers to store in VRAM for the draft model (-1 - use default)
     int32_t main_gpu                        = 0;    // the GPU that is used for scratch and small tensors
     float   tensor_split[LLAMA_MAX_DEVICES] = {0};  // how split tensors should be distributed across GPUs
-    int32_t n_probs                         = 0;    // if greater than 0, output the probabilities of top n_probs tokens.
     int32_t n_beams                         = 0;    // if non-zero then use beam search of given width.
     float   rope_freq_base                  = 0.0f; // RoPE base frequency
     float   rope_freq_scale                 = 0.0f; // RoPE frequency scaling factor
 
-    // sampling parameters
+    // // sampling parameters
-    int32_t top_k             = 40;    // <= 0 to use vocab size
+    struct llama_sampling_params sparams;
-    float   top_p             = 0.95f; // 1.0 = disabled
-    float   tfs_z             = 1.00f; // 1.0 = disabled
-    float   typical_p         = 1.00f; // 1.0 = disabled
-    float   temp              = 0.80f; // 1.0 = disabled
-    float   repeat_penalty    = 1.10f; // 1.0 = disabled
-    int32_t repeat_last_n     = 64;    // last n tokens to penalize (0 = disable penalty, -1 = context size)
-    float   frequency_penalty = 0.00f; // 0.0 = disabled
-    float   presence_penalty  = 0.00f; // 0.0 = disabled
-    int32_t mirostat          = 0;     // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
-    float   mirostat_tau      = 5.00f; // target entropy
-    float   mirostat_eta      = 0.10f; // learning rate
-
-    std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
-
-    // Classifier-Free Guidance
-    // https://arxiv.org/abs/2306.17806
-    std::string cfg_negative_prompt;       // string to help guidance
-    float       cfg_scale         = 1.f;   // How strong is guidance
 
     std::string model             = "models/7B/ggml-model-f16.gguf"; // model path
     std::string model_draft       = "";                              // draft model for speculative decoding
@@ -83,10 +66,10 @@ struct gpt_params {
     std::string path_prompt_cache = "";  // path to file for saving/loading prompt eval state
     std::string input_prefix      = "";  // string to prefix user inputs with
     std::string input_suffix      = "";  // string to suffix user inputs with
-    std::string grammar           = "";  // optional BNF-like grammar to constrain sampling
     std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
     std::string logdir            = "";  // directory in which to save YAML log files
 
+    // TODO: avoid tuple, use struct
     std::vector<std::tuple<std::string, float>> lora_adapter; // lora adapter path with user defined scale
     std::string lora_base  = "";                              // base model path for the lora adapter
 
@@ -115,13 +98,16 @@ struct gpt_params {
     bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
     bool ignore_eos        = false; // ignore generated EOS tokens
     bool instruct          = false; // instruction mode (used for Alpaca models)
-    bool penalize_nl       = true;  // consider newlines as a repeatable token
     bool logits_all        = false; // return logits for all tokens in the batch
     bool use_mmap          = true;  // use mmap for faster loads
     bool use_mlock         = false; // use mlock to keep model in memory
     bool numa              = false; // attempt optimizations that help on some NUMA systems
     bool verbose_prompt    = false; // print prompt tokens before generation
     bool infill            = false; // use infill mode
+
+    // multimodal models (see examples/llava)
+    std::string mmproj = ""; // path to multimodal projector
+    std::string image = ""; // path to an image file
 };
 
 bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
@@ -138,10 +124,23 @@ void process_escapes(std::string& input);
 // Model utils
 //
 
+// TODO: avoid tuplue, use struct
 std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(gpt_params & params);
-struct llama_model_params   llama_model_params_from_gpt_params(const gpt_params & params);
+
+struct llama_model_params   llama_model_params_from_gpt_params  (const gpt_params & params);
 struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params);
 
+// Batch utils
+
+void llama_batch_clear(struct llama_batch & batch);
+
+void llama_batch_add(
+                 struct llama_batch & batch,
+                        llama_token   id,
+                          llama_pos   pos,
+    const std::vector<llama_seq_id> & seq_ids,
+                               bool   logits);
+
 //
 // Vocab utils
 //
@@ -151,12 +150,14 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
 std::vector<llama_token> llama_tokenize(
   const struct llama_context * ctx,
            const std::string & text,
-                        bool   add_bos);
+                        bool   add_bos,
+                        bool   special = false);
 
 std::vector<llama_token> llama_tokenize(
     const struct llama_model * model,
            const std::string & text,
-                        bool   add_bos);
+                        bool   add_bos,
+                        bool   special = false);
 
 // tokenizes a token into a piece
 // should work similar to Python's `tokenizer.id_to_piece`
@@ -180,36 +181,6 @@ std::string llama_detokenize_bpe(
                          llama_context * ctx,
         const std::vector<llama_token> & tokens);
 
-//
-// Sampling utils
-//
-
-// this is a common sampling function used across the examples for convenience
-// it can serve as a starting point for implementing your own sampling function
-//
-// required:
-//  - ctx:    context to use for sampling
-//  - params: sampling parameters
-//
-// optional:
-//  - ctx_guidance:  context to use for classifier-free guidance, ignore if NULL
-//  - grammar:       grammar to use for sampling, ignore if NULL
-//  - last_tokens:   needed for repetition penalty, ignore if empty
-//  - idx:           sample from llama_get_logits_ith(ctx, idx)
-//
-// returns:
-//  - token:      sampled token
-//  - candidates: vector of candidate tokens
-//
-llama_token llama_sample_token(
-                  struct llama_context * ctx,
-                  struct llama_context * ctx_guidance,
-                  struct llama_grammar * grammar,
-               const struct gpt_params & params,
-        const std::vector<llama_token> & last_tokens,
-         std::vector<llama_token_data> & candidates,
-                                   int   idx = 0);
-
 //
 // YAML utils
 //

common/grammar-parser.cpp

@@ -399,7 +399,7 @@ namespace grammar_parser {
     void print_grammar(FILE * file, const parse_state & state) {
         try {
             std::map<uint32_t, std::string> symbol_id_names;
-            for (auto kv : state.symbol_ids) {
+            for (const auto & kv : state.symbol_ids) {
                 symbol_id_names[kv.second] = kv.first;
             }
             for (size_t i = 0, end = state.rules.size(); i < end; i++) {

common/log.h

@@ -579,38 +579,75 @@ inline std::string log_var_to_string_impl(const std::vector<int> & var)
     return buf.str();
 }
 
-#define LOG_TOKENS_TOSTR_PRETTY(ctx, tokens)                                 \
+template <typename C, typename T>
-    [&tokens, &ctx]()                                                        \
+inline std::string LOG_TOKENS_TOSTR_PRETTY(const C & ctx, const T & tokens)
-    {                                                                        \
+{
-        std::stringstream buf;                                               \
+    std::stringstream buf;
-        buf << "[ ";                                                         \
+    buf << "[ ";
-                                                                             \
+
-        bool first = true;                                                   \
+    bool first = true;
-        for (const auto &token : tokens)                                     \
+    for (const auto &token : tokens)
-        {                                                                    \
+    {
-            if (!first)                                                      \
+        if (!first) {
-                buf << ", ";                                                 \
+            buf << ", ";
-            else                                                             \
+        } else {
-                first = false;                                               \
+            first = false;
-                                                                             \
+        }
-            auto detokenized = llama_token_to_piece(ctx, token);             \
+
-                                                                             \
+        auto detokenized = llama_token_to_piece(ctx, token);
-            detokenized.erase(                                               \
+
-                std::remove_if(                                              \
+        detokenized.erase(
-                    detokenized.begin(),                                     \
+            std::remove_if(
-                    detokenized.end(),                                       \
+                detokenized.begin(),
-                    [](const unsigned char c) { return !std::isprint(c); }), \
+                detokenized.end(),
-                detokenized.end());                                          \
+                [](const unsigned char c) { return !std::isprint(c); }),
-                                                                             \
+            detokenized.end());
-            buf                                                              \
+
-                << "'" << detokenized << "'"                                 \
+        buf
-                << ":" << std::to_string(token);                             \
+            << "'" << detokenized << "'"
-        }                                                                    \
+            << ":" << std::to_string(token);
-        buf << " ]";                                                         \
+    }
-                                                                             \
+    buf << " ]";
-        return buf.str();                                                    \
+
-    }()                                                                      \
+    return buf.str();
-        .c_str()
+}
+
+template <typename C, typename B>
+inline std::string LOG_BATCH_TOSTR_PRETTY(const C & ctx, const B & batch)
+{
+    std::stringstream buf;
+    buf << "[ ";
+
+    bool first = true;
+    for (int i = 0; i < batch.n_tokens; ++i)
+    {
+        if (!first) {
+            buf << ", ";
+        } else {
+            first = false;
+        }
+
+        auto detokenized = llama_token_to_piece(ctx, batch.token[i]);
+
+        detokenized.erase(
+            std::remove_if(
+                detokenized.begin(),
+                detokenized.end(),
+                [](const unsigned char c) { return !std::isprint(c); }),
+            detokenized.end());
+
+        buf
+            << "\n" << std::to_string(i)
+            << ":token '" << detokenized << "'"
+            << ":pos " << std::to_string(batch.pos[i])
+            << ":n_seq_id  " << std::to_string(batch.n_seq_id[i])
+            << ":seq_id " << std::to_string(batch.seq_id[i][0])
+            << ":logits " << std::to_string(batch.logits[i]);
+    }
+    buf << " ]";
+
+    return buf.str();
+}
 
 #ifdef LOG_DISABLE_LOGS
 

common/sampling.cpp

@@ -0,0 +1,222 @@
+#include "sampling.h"
+
+struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params) {
+    struct llama_sampling_context * result = new llama_sampling_context();
+
+    result->params  = params;
+    result->grammar = nullptr;
+
+    // if there is a grammar, parse it
+    if (!params.grammar.empty()) {
+        result->parsed_grammar = grammar_parser::parse(params.grammar.c_str());
+
+        // will be empty (default) if there are parse errors
+        if (result->parsed_grammar.rules.empty()) {
+            fprintf(stderr, "%s: failed to parse grammar\n", __func__);
+            return nullptr;
+        }
+
+        std::vector<const llama_grammar_element *> grammar_rules(result->parsed_grammar.c_rules());
+
+        result->grammar = llama_grammar_init(
+                grammar_rules.data(),
+                grammar_rules.size(), result->parsed_grammar.symbol_ids.at("root"));
+    }
+
+    result->prev.resize(params.n_prev);
+
+    return result;
+}
+
+void llama_sampling_free(struct llama_sampling_context * ctx) {
+    if (ctx->grammar != NULL) {
+        llama_grammar_free(ctx->grammar);
+    }
+
+    delete ctx;
+}
+
+void llama_sampling_reset(llama_sampling_context * ctx) {
+    if (ctx->grammar != NULL) {
+        llama_grammar_free(ctx->grammar);
+    }
+
+    if (!ctx->parsed_grammar.rules.empty()) {
+        std::vector<const llama_grammar_element *> grammar_rules(ctx->parsed_grammar.c_rules());
+
+        ctx->grammar = llama_grammar_init(
+                grammar_rules.data(),
+                grammar_rules.size(), ctx->parsed_grammar.symbol_ids.at("root"));
+    }
+
+    std::fill(ctx->prev.begin(), ctx->prev.end(), 0);
+    ctx->cur.clear();
+}
+
+void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst) {
+    if (dst->grammar) {
+        llama_grammar_free(dst->grammar);
+        dst->grammar = nullptr;
+    }
+
+    if (src->grammar) {
+        dst->grammar = llama_grammar_copy(src->grammar);
+    }
+
+    dst->prev = src->prev;
+}
+
+llama_token llama_sampling_last(llama_sampling_context * ctx) {
+    return ctx->prev.back();
+}
+
+std::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama_context * ctx_main, int n) {
+    const int size = ctx_sampling->prev.size();
+
+    n = std::min(n, size);
+
+    std::string result;
+
+    for (int i = size - n; i < size; i++) {
+        result += llama_token_to_piece(ctx_main, ctx_sampling->prev[i]);
+    }
+
+    return result;
+}
+
+std::string llama_sampling_print(const llama_sampling_params & params) {
+    char result[1024];
+
+    snprintf(result, sizeof(result),
+            "\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
+            "\ttop_k = %d, tfs_z = %.3f, top_p = %.3f, typical_p = %.3f, temp = %.3f\n"
+            "\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f",
+            params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present,
+            params.top_k, params.tfs_z, params.top_p, params.typical_p, params.temp,
+            params.mirostat, params.mirostat_eta, params.mirostat_tau);
+
+    return std::string(result);
+}
+
+llama_token llama_sampling_sample(
+                  struct llama_sampling_context * ctx_sampling,
+                  struct llama_context * ctx_main,
+                  struct llama_context * ctx_cfg,
+                  const int idx) {
+    const llama_sampling_params & params = ctx_sampling->params;
+
+    const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
+
+    const float   temp            = params.temp;
+    const int32_t top_k           = params.top_k <= 0 ? n_vocab : params.top_k;
+    const float   top_p           = params.top_p;
+    const float   tfs_z           = params.tfs_z;
+    const float   typical_p       = params.typical_p;
+    const int32_t penalty_last_n  = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
+    const float   penalty_repeat  = params.penalty_repeat;
+    const float   penalty_freq    = params.penalty_freq;
+    const float   penalty_present = params.penalty_present;
+    const int     mirostat        = params.mirostat;
+    const float   mirostat_tau    = params.mirostat_tau;
+    const float   mirostat_eta    = params.mirostat_eta;
+    const bool    penalize_nl     = params.penalize_nl;
+
+    auto & prev = ctx_sampling->prev;
+    auto & cur  = ctx_sampling->cur;
+
+    llama_token id = 0;
+
+    float * logits = llama_get_logits_ith(ctx_main, idx);
+
+    // apply params.logit_bias map
+    for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
+        logits[it->first] += it->second;
+    }
+
+    cur.clear();
+
+    for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+        cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+    }
+
+    llama_token_data_array cur_p = { cur.data(), cur.size(), false };
+
+    if (ctx_cfg) {
+        llama_sample_classifier_free_guidance(ctx_main, &cur_p, ctx_cfg, params.cfg_scale);
+    }
+
+    // apply penalties
+    if (!prev.empty()) {
+        const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))];
+
+        llama_sample_repetition_penalties(ctx_main, &cur_p,
+                prev.data() + prev.size() - penalty_last_n,
+                penalty_last_n, penalty_repeat, penalty_freq, penalty_present);
+
+        if (!penalize_nl) {
+            for (size_t idx = 0; idx < cur_p.size; idx++) {
+                if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) {
+                    cur_p.data[idx].logit = nl_logit;
+                    break;
+                }
+            }
+        }
+    }
+
+    if (ctx_sampling->grammar != NULL) {
+        llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);
+    }
+
+    if (temp <= 0) {
+        // greedy sampling
+        id = llama_sample_token_greedy(ctx_main, &cur_p);
+    } else {
+        if (mirostat == 1) {
+            const int mirostat_m = 100;
+            llama_sample_temp(ctx_main, &cur_p, temp);
+            id = llama_sample_token_mirostat(ctx_main, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &ctx_sampling->mirostat_mu);
+        } else if (mirostat == 2) {
+            llama_sample_temp(ctx_main, &cur_p, temp);
+            id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu);
+        } else {
+            // temperature sampling
+            size_t min_keep = std::max(1, params.n_probs);
+
+            llama_sample_top_k    (ctx_main, &cur_p, top_k,     min_keep);
+            llama_sample_tail_free(ctx_main, &cur_p, tfs_z,     min_keep);
+            llama_sample_typical  (ctx_main, &cur_p, typical_p, min_keep);
+            llama_sample_top_p    (ctx_main, &cur_p, top_p,     min_keep);
+            llama_sample_temp     (ctx_main, &cur_p, temp);
+
+            id = llama_sample_token(ctx_main, &cur_p);
+
+            //{
+            //    const int n_top = 10;
+            //    LOG("top %d candidates:\n", n_top);
+
+            //    for (int i = 0; i < n_top; i++) {
+            //        const llama_token id = cur_p.data[i].id;
+            //        (void)id; // To avoid a warning that id is unused when logging is disabled.
+            //        LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx_main, id).c_str(), cur_p.data[i].p);
+            //    }
+            //}
+
+            LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str());
+        }
+    }
+
+    return id;
+}
+
+void llama_sampling_accept(
+        struct llama_sampling_context * ctx_sampling,
+        struct llama_context * ctx_main,
+        llama_token id,
+        bool apply_grammar) {
+    ctx_sampling->prev.erase(ctx_sampling->prev.begin());
+    ctx_sampling->prev.push_back(id);
+
+    if (ctx_sampling->grammar != NULL && apply_grammar) {
+        llama_grammar_accept_token(ctx_main, ctx_sampling->grammar, id);
+    }
+}

common/sampling.h

@@ -0,0 +1,109 @@
+#pragma once
+
+#include "llama.h"
+
+#include "grammar-parser.h"
+
+#include <string>
+#include <vector>
+#include <unordered_map>
+
+// sampling parameters
+typedef struct llama_sampling_params {
+    int32_t n_prev            = 64;    // number of previous tokens to remember
+    int32_t n_probs           = 0;     // if greater than 0, output the probabilities of top n_probs tokens.
+    int32_t top_k             = 40;    // <= 0 to use vocab size
+    float   top_p             = 0.95f; // 1.0 = disabled
+    float   tfs_z             = 1.00f; // 1.0 = disabled
+    float   typical_p         = 1.00f; // 1.0 = disabled
+    float   temp              = 0.80f; // 1.0 = disabled
+    int32_t penalty_last_n    = 64;    // last n tokens to penalize (0 = disable penalty, -1 = context size)
+    float   penalty_repeat    = 1.10f; // 1.0 = disabled
+    float   penalty_freq      = 0.00f; // 0.0 = disabled
+    float   penalty_present   = 0.00f; // 0.0 = disabled
+    int32_t mirostat          = 0;     // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
+    float   mirostat_tau      = 5.00f; // target entropy
+    float   mirostat_eta      = 0.10f; // learning rate
+    bool    penalize_nl       = true;  // consider newlines as a repeatable token
+
+    std::string grammar;  // optional BNF-like grammar to constrain sampling
+
+    // Classifier-Free Guidance
+    // https://arxiv.org/abs/2306.17806
+    std::string cfg_negative_prompt; // string to help guidance
+    float       cfg_scale     = 1.f; // how strong is guidance
+
+    std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
+} llama_sampling_params;
+
+// general sampler context
+// TODO: move to llama.h
+struct llama_sampling_context {
+    // parameters that will be used for sampling
+    llama_sampling_params params;
+
+    // mirostat sampler state
+    float mirostat_mu;
+
+    llama_grammar * grammar;
+
+    // internal
+    grammar_parser::parse_state parsed_grammar;
+
+    // TODO: replace with ring-buffer
+    std::vector<llama_token>      prev;
+    std::vector<llama_token_data> cur;
+};
+
+#include "common.h"
+
+// Create a new sampling context instance.
+struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params);
+
+void llama_sampling_free(struct llama_sampling_context * ctx);
+
+// Reset the sampler context
+// - clear prev tokens
+// - reset grammar
+void llama_sampling_reset(llama_sampling_context * ctx);
+
+// Copy the sampler context
+void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst);
+
+// Get the last sampled token
+llama_token llama_sampling_last(llama_sampling_context * ctx);
+
+// Get a string representation of the last sampled tokens
+std::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama_context * ctx_main, int n);
+
+// Print sampling parameters into a string
+std::string llama_sampling_print(const llama_sampling_params & params);
+
+// this is a common sampling function used across the examples for convenience
+// it can serve as a starting point for implementing your own sampling function
+// Note: When using multiple sequences, it is the caller's responsibility to call
+//       llama_sampling_reset when a sequence ends
+//
+// required:
+//  - ctx_main:     context to use for sampling
+//  - ctx_sampling: sampling-specific context
+//
+// optional:
+//  - ctx_cfg:      context to use for classifier-free guidance
+//  - idx:          sample from llama_get_logits_ith(ctx, idx)
+//
+// returns:
+//  - token:      sampled token
+//  - candidates: vector of candidate tokens
+//
+llama_token llama_sampling_sample(
+        struct llama_sampling_context * ctx_sampling,
+        struct llama_context * ctx_main,
+        struct llama_context * ctx_cfg,
+        int idx = 0);
+
+void llama_sampling_accept(
+        struct llama_sampling_context * ctx_sampling,
+        struct llama_context * ctx_main,
+        llama_token id,
+        bool apply_grammar);

common/train.cpp

@@ -236,8 +236,8 @@ int64_t get_example_targets_batch(
     int64_t used_samples = 0;
 
     ggml_set_f32(target_probs, 0.0f);
-    llama_token bos = llama_token_bos(lctx);
+    llama_token bos = llama_token_bos(llama_get_model(lctx));
-    llama_token eos = llama_token_eos(lctx);
+    llama_token eos = llama_token_eos(llama_get_model(lctx));
     // printf("%s: example_id=%d n_batch=%d n_train_samples=%zu\n", __func__, example_id, n_batch, n_train_samples);
     for (int k=0; k<n_batch; ++k) {
         // printf("%s: batch %d\n", __func__, k);
@@ -863,7 +863,7 @@ size_t tokenize_file(
             (int) buf.size(),
             out_tokens.data(),
             (int) out_tokens.size(),
-            false);
+            false, false);
         if (n_tokens < 0) {
             out_tokens.resize(-n_tokens);
             n_tokens = llama_tokenize(
@@ -872,7 +872,7 @@ size_t tokenize_file(
                 (int) buf.size(),
                 out_tokens.data(),
                 (int) out_tokens.size(),
-                false);
+                false, false);
         }
         if (n_tokens >= 0) {
             out_tokens.resize(n_tokens);
@@ -924,7 +924,7 @@ size_t tokenize_file(
         for (llama_token token=0; token < n_vocab; ++token) {
             max_token_text_size = std::max(
                 max_token_text_size,
-                strlen(llama_token_get_text(lctx, token)));
+                strlen(llama_token_get_text(llama_get_model(lctx), token)));
         }
 
         // upper bound of context byte length.
@@ -966,7 +966,7 @@ size_t tokenize_file(
                     (int) buf_sample.size(),
                     tok_sample.data(),
                     (int) tok_sample.size(),
-                    false);
+                    false, false);
                 if (n_tokens < 0) {
                     tok_sample.resize(-n_tokens);
                     n_tokens = llama_tokenize(llama_get_model(lctx),
@@ -974,7 +974,7 @@ size_t tokenize_file(
                         (int) buf_sample.size(),
                         tok_sample.data(),
                         (int) tok_sample.size(),
-                        false);
+                        false, false);
                     GGML_ASSERT(n_tokens >= 0);
                 }
                 GGML_ASSERT(n_tokens <= (int) tok_sample.size());
@@ -1425,7 +1425,7 @@ void train_opt_callback(void * vdata, int accum_step, float * sched, bool * canc
 
         int impr_plot = -(int)(1 + (opt->loss_before - opt->loss_after) * 10.0f + 0.5f);
         if (impr_plot > 0) impr_plot = 0;
-        if (std::isnan(opt->loss_before) || std::isnan(opt->loss_before)) impr_plot = 0;
+        if (std::isnan(opt->loss_before) || std::isnan(opt->loss_after)) impr_plot = 0;
         printf("%s: iter=%6d sample=%zu/%zu sched=%f loss=%f",
             __func__, opt->iter, std::min(1+train->shuffle_next_sample, train->shuffle_sample_count), train->shuffle_sample_count,
             *sched, opt->loss_after);

convert-baichuan-hf-to-gguf.py

@@ -76,6 +76,7 @@ def parse_args() -> argparse.Namespace:
         "ftype", type=int, choices=[0, 1], default=1, nargs='?',
         help="output format - use 0 for float32, 1 for float16",
     )
+    parser.add_argument("--bigendian",   action="store_true",    help="model is executed on big endian machine")
     return parser.parse_args()
 
 args = parse_args()
@@ -86,6 +87,11 @@ if not dir_model.is_dir():
     print(f'Error: {args.model} is not a directory', file = sys.stderr)
     sys.exit(1)
 
+endianess = gguf.GGUFEndian.LITTLE
+if args.bigendian:
+    endianess = gguf.GGUFEndian.BIG
+endianess_str = "Big Endian" if args.bigendian else "Little Endian"
+print(f"gguf: Conversion Endianess {endianess}")
 # possible tensor data types
 #   ftype == 0 -> float32
 #   ftype == 1 -> float16
@@ -104,7 +110,7 @@ print("gguf: loading model "+dir_model.name)
 with open(dir_model / "config.json", "r", encoding="utf-8") as f:
     hparams = json.load(f)
 print("hello print: ",hparams["architectures"][0])
-if hparams["architectures"][0] != "BaichuanForCausalLM":
+if hparams["architectures"][0] != "BaichuanForCausalLM" and hparams["architectures"][0] != "BaiChuanForCausalLM":
     print("Model architecture not supported: " + hparams["architectures"][0])
 
     sys.exit()
@@ -113,7 +119,7 @@ if hparams["architectures"][0] != "BaichuanForCausalLM":
 num_parts = count_model_parts(dir_model)
 print(f"num_parts:{num_parts}\n")
 ARCH=gguf.MODEL_ARCH.BAICHUAN
-gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
+gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess)
 
 print("gguf: get model metadata")
 
@@ -224,7 +230,7 @@ gguf_writer.add_token_list(tokens)
 gguf_writer.add_token_scores(scores)
 gguf_writer.add_token_types(toktypes)
 
-special_vocab = gguf.SpecialVocab(dir_model)
+special_vocab = gguf.SpecialVocab(dir_model, n_vocab = len(tokens))
 special_vocab.add_to_gguf(gguf_writer)
 
 # TENSORS

convert-bloom-hf-to-gguf.py

@@ -0,0 +1,247 @@
+#!/usr/bin/env python3
+# HF bloom --> gguf conversion
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import re
+import struct
+import sys
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+from transformers import AutoTokenizer  # type: ignore[import]
+
+if 'NO_LOCAL_GGUF' not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
+import gguf
+
+
+def count_model_parts(dir_model: Path) -> int:
+    num_parts = 0
+    for filename in os.listdir(dir_model):
+        if filename.startswith("pytorch_model-"):
+            num_parts += 1
+
+    if num_parts > 0:
+        print("gguf: found " + str(num_parts) + " model parts")
+    return num_parts
+
+
+# Supported Models:
+#   https://huggingface.co/bigscience/bloom-1b7
+#   https://huggingface.co/bigscience/bloom-3b
+#   https://huggingface.co/bigscience/bloom-7b1
+#   https://huggingface.co/Langboat/bloom-1b4-zh
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Convert a Bloom model to a GGML compatible file")
+    parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
+    parser.add_argument("--outfile",    type=Path,           help="path to write to; default: based on input")
+    parser.add_argument("model",        type=Path,           help="directory containing model file, or model file itself (*.bin)")
+    parser.add_argument("ftype",        type=int,            help="output format - use 0 for float32, 1 for float16", choices=[0, 1], default = 1)
+    return parser.parse_args()
+
+args = parse_args()
+
+dir_model = args.model
+ftype = args.ftype
+if not dir_model.is_dir():
+    print(f'Error: {args.model} is not a directory', file = sys.stderr)
+    sys.exit(1)
+
+# possible tensor data types
+#   ftype == 0 -> float32
+#   ftype == 1 -> float16
+
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+
+if args.outfile is not None:
+    fname_out = args.outfile
+else:
+    # output in the same directory as the model by default
+    fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
+
+print("gguf: loading model "+dir_model.name)
+
+with open(dir_model / "config.json", "r", encoding="utf-8") as f:
+    hparams = json.load(f)
+
+if hparams["architectures"][0] != "BloomForCausalLM":
+    print("Model architecture not supported: " + hparams["architectures"][0])
+    sys.exit(1)
+
+# get number of model parts
+num_parts = count_model_parts(dir_model)
+
+ARCH=gguf.MODEL_ARCH.BLOOM
+gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
+
+print("gguf: get model metadata")
+
+block_count = hparams["n_layer"]
+
+gguf_writer.add_name("Bloom")
+n_embed = hparams.get("hidden_size", hparams.get("n_embed"))
+n_head = hparams.get("n_head", hparams.get("num_attention_heads"))
+gguf_writer.add_context_length(hparams.get("seq_length", n_embed))
+gguf_writer.add_embedding_length(n_embed)
+gguf_writer.add_feed_forward_length(4 * n_embed)
+gguf_writer.add_block_count(block_count)
+gguf_writer.add_head_count(n_head)
+gguf_writer.add_head_count_kv(n_head)
+gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
+gguf_writer.add_file_type(ftype)
+
+# TOKENIZATION
+
+print("gguf: get tokenizer metadata")
+
+tokens: list[bytearray] = []
+scores: list[float] = []
+toktypes: list[int] = []
+
+# gpt2 tokenizer
+gguf_writer.add_tokenizer_model("gpt2")
+
+print("gguf: get gpt2 tokenizer vocab")
+
+# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
+tokenizer = AutoTokenizer.from_pretrained(dir_model)
+
+# The number of tokens in tokenizer.json can differ from the expected vocab size.
+# This causes downstream issues with mismatched tensor sizes when running the inference
+vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
+assert max(tokenizer.vocab.values()) < vocab_size
+
+added_vocab = tokenizer.get_added_vocab()
+reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
+
+for i in range(vocab_size):
+    if i not in reverse_vocab:
+        tokens.append(f"[PAD{i}]")
+        toktypes.append(gguf.TokenType.USER_DEFINED)
+    elif reverse_vocab[i] in added_vocab:
+        tokens.append(reverse_vocab[i])
+        if tokenizer.added_tokens_decoder[i].special:
+            toktypes.append(gguf.TokenType.CONTROL)
+        else:
+            toktypes.append(gguf.TokenType.USER_DEFINED)
+    else:
+        tokens.append(reverse_vocab[i])
+        toktypes.append(gguf.TokenType.NORMAL)
+
+gguf_writer.add_token_list(tokens)
+gguf_writer.add_token_types(toktypes)
+
+special_vocab = gguf.SpecialVocab(dir_model, load_merges=True, n_vocab = len(tokens))
+special_vocab.add_to_gguf(gguf_writer)
+
+# TENSORS
+
+tensor_map = gguf.get_tensor_name_map(ARCH, block_count)
+
+# params for qkv transform
+n_head_kv = hparams.get("n_head_kv", n_head)
+head_dim = n_embed // n_head
+
+# tensor info
+print("gguf: get tensor metadata")
+
+if num_parts == 0:
+    part_names = iter(("pytorch_model.bin",))
+else:
+    part_names = (
+        f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
+    )
+
+for part_name in part_names:
+    if args.vocab_only:
+        break
+    print("gguf: loading model part '" + part_name + "'")
+    model_part = torch.load(dir_model / part_name, map_location="cpu")
+
+    has_lm_head = True
+    if "lm_head.weight" not in model_part.keys() and "output.weight" not in model_part.keys():
+        has_lm_head = False
+
+    for original_name in model_part.keys():
+        data = model_part[original_name]
+        name = re.sub(r'transformer\.', '', original_name)
+
+        old_dtype = data.dtype
+
+        # convert any unsupported data types to float32
+        if data.dtype != torch.float16 and data.dtype != torch.float32:
+            data = data.to(torch.float32)
+
+        data = data.squeeze().numpy()
+
+        if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name):
+            # Map bloom-style qkv_linear to gpt-style qkv_linear
+            # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252  # noqa
+            # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312  # noqa
+            qkv_weights = data.reshape((n_head, 3, n_embed // n_head, n_embed))
+            data = np.concatenate(
+                (qkv_weights[:, 0, :, :].reshape((-1, n_embed)),
+                 qkv_weights[:, 1, :, :].reshape((-1, n_embed)),
+                 qkv_weights[:, 2, :, :].reshape((-1, n_embed))),
+                axis=0
+            )
+            print("re-format attention.linear_qkv.weight")
+        elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name):
+            qkv_bias = data.reshape((n_head, 3, n_embed // n_head))
+            data = np.concatenate(
+                (qkv_bias[:, 0, :].reshape((n_embed,)),
+                 qkv_bias[:, 1, :].reshape((n_embed,)),
+                 qkv_bias[:, 2, :].reshape((n_embed,))),
+                axis=0
+            )
+            print("re-format attention.linear_qkv.bias")
+
+        # map tensor names
+        new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
+        if new_name is None:
+            print("Can not map tensor '" + name + "'")
+            sys.exit()
+
+        n_dims = len(data.shape)
+        data_dtype = data.dtype
+
+        # if f32 desired, convert any float16 to float32
+        if ftype == 0 and data_dtype == np.float16:
+            data = data.astype(np.float32)
+
+        # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
+        if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+            data = data.astype(np.float32)
+
+        # if f16 desired, convert any float32 2-dim weight tensors to float16
+        if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+            data = data.astype(np.float16)
+
+        print(name, "=>", new_name + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype))
+
+        gguf_writer.add_tensor(new_name, data)
+
+        if not has_lm_head and name == "word_embeddings.weight":
+            gguf_writer.add_tensor("output.weight", data)
+            print(name, "=>", "output.weight" + ", shape = " + str(data.shape) + ", " + str(old_dtype) + " --> " + str(data.dtype))  # noqa
+
+
+print("gguf: write header")
+gguf_writer.write_header_to_file()
+print("gguf: write metadata")
+gguf_writer.write_kv_data_to_file()
+if not args.vocab_only:
+    print("gguf: write tensors")
+    gguf_writer.write_tensors_to_file()
+
+gguf_writer.close()
+
+print(f"gguf: model successfully exported to '{fname_out}'")
+print("")

convert-falcon-hf-to-gguf.py

@@ -78,7 +78,7 @@ print("gguf: loading model "+dir_model.name)
 with open(dir_model / "config.json", "r", encoding="utf-8") as f:
     hparams = json.load(f)
 
-if hparams["architectures"][0] != "FalconForCausalLM":
+if hparams["architectures"][0] not in ("RWForCausalLM", "FalconForCausalLM"):
     print("Model architecture not supported: " + hparams["architectures"][0])
 
     sys.exit(1)
@@ -97,7 +97,17 @@ gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
 
 print("gguf: get model metadata")
 
-block_count = hparams["num_hidden_layers"]
+block_count = hparams.get("num_hidden_layers")
+if block_count is None:
+    block_count = hparams["n_layer"]  # old name
+
+n_head = hparams.get("num_attention_heads")
+if n_head is None:
+    n_head = hparams["n_head"]  # old name
+
+n_head_kv = hparams.get("num_kv_heads")
+if n_head_kv is None:
+    n_head_kv = hparams.get("n_head_kv", 1)  # old name
 
 gguf_writer.add_name("Falcon")
 gguf_writer.add_context_length(2048) # not in config.json
@@ -105,11 +115,8 @@ gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform
 gguf_writer.add_embedding_length(hparams["hidden_size"])
 gguf_writer.add_feed_forward_length(4 * hparams["hidden_size"])
 gguf_writer.add_block_count(block_count)
-gguf_writer.add_head_count(hparams["num_attention_heads"])
+gguf_writer.add_head_count(n_head)
-if "num_kv_heads" in hparams:
+gguf_writer.add_head_count_kv(n_head_kv)
-    gguf_writer.add_head_count_kv(hparams["num_kv_heads"])
-else:
-    gguf_writer.add_head_count_kv(1)
 gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
 gguf_writer.add_file_type(ftype)
 
@@ -145,17 +152,13 @@ gguf_writer.add_token_list(tokens)
 gguf_writer.add_token_scores(scores)
 gguf_writer.add_token_types(toktypes)
 
-special_vocab = gguf.SpecialVocab(dir_model, load_merges = True)
+special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens))
 special_vocab.add_to_gguf(gguf_writer)
 
 # TENSORS
 
 tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
 
-# params for qkv transform
-n_head    = hparams["num_attention_heads"]
-n_head_kv = hparams["num_kv_heads"] if "num_kv_heads" in hparams else 1
-
 head_dim = hparams["hidden_size"] // n_head
 
 # tensor info

convert-gptneox-hf-to-gguf.py

@@ -123,18 +123,27 @@ tokenizer = AutoTokenizer.from_pretrained(dir_model)
 vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
 assert max(tokenizer.vocab.values()) < vocab_size
 
+added_vocab = tokenizer.get_added_vocab()
 reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
 
 for i in range(vocab_size):
-    tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]")
+    if i not in reverse_vocab:
-    scores.append(0.0) # dummy
+        tokens.append(f"[PAD{i}]")
-    toktypes.append(gguf.TokenType.NORMAL)
+        toktypes.append(gguf.TokenType.USER_DEFINED)
+    elif reverse_vocab[i] in added_vocab:
+        tokens.append(reverse_vocab[i])
+        if tokenizer.added_tokens_decoder[i].special:
+            toktypes.append(gguf.TokenType.CONTROL)
+        else:
+            toktypes.append(gguf.TokenType.USER_DEFINED)
+    else:
+        tokens.append(reverse_vocab[i])
+        toktypes.append(gguf.TokenType.NORMAL)
 
 gguf_writer.add_token_list(tokens)
-gguf_writer.add_token_scores(scores)
 gguf_writer.add_token_types(toktypes)
 
-special_vocab = gguf.SpecialVocab(dir_model, load_merges = True)
+special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens))
 special_vocab.add_to_gguf(gguf_writer)
 
 # TENSORS

convert-llama-ggml-to-gguf.py

@@ -388,7 +388,9 @@ def handle_metadata(cfg, hp):
         cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir,
         cfg.vocabtype )
     # FIXME: Respect cfg.vocab_dir?
-    svocab = gguf.SpecialVocab(cfg.model_metadata_dir)
+    svocab = gguf.SpecialVocab(cfg.model_metadata_dir,
+        load_merges = cfg.vocabtype == 'bpe',
+        n_vocab = vocab.vocab_size)
     convert.check_vocab_size(params, vocab)
     return (params, vocab, svocab)
 

convert-mpt-hf-to-gguf.py

@@ -0,0 +1,227 @@
+#!/usr/bin/env python3
+# HF mpt--> gguf conversion
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import struct
+import sys
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+from transformers import AutoTokenizer  # type: ignore[import]
+
+if 'NO_LOCAL_GGUF' not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
+import gguf
+
+
+def count_model_parts(dir_model: Path) -> int:
+    num_parts = 0
+    for filename in os.listdir(dir_model):
+        if filename.startswith("pytorch_model-"):
+            num_parts += 1
+
+    if num_parts > 0:
+        print("gguf: found " + str(num_parts) + " model parts")
+    return num_parts
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Convert an MPT model to a GGML compatible file")
+    parser.add_argument(
+        "--vocab-only", action="store_true",
+        help="extract only the vocab",
+    )
+    parser.add_argument(
+        "--outfile", type=Path,
+        help="path to write to; default: based on input",
+    )
+    parser.add_argument(
+        "model", type=Path,
+        help="directory containing model file, or model file itself (*.bin)",
+    )
+    parser.add_argument(
+        "ftype", type=int, choices=[0, 1], default=1, nargs='?',
+        help="output format - use 0 for float32, 1 for float16",
+    )
+    return parser.parse_args()
+
+args = parse_args()
+
+dir_model = args.model
+ftype = args.ftype
+if not dir_model.is_dir():
+    print(f'Error: {args.model} is not a directory', file = sys.stderr)
+    sys.exit(1)
+
+# possible tensor data types
+#   ftype == 0 -> float32
+#   ftype == 1 -> float16
+
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+
+if args.outfile is not None:
+    fname_out = args.outfile
+else:
+    # output in the same directory as the model by default
+    fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
+
+print("gguf: loading model "+dir_model.name)
+
+with open(dir_model / "config.json", "r", encoding="utf-8") as f:
+    hparams = json.load(f)
+
+if hparams["architectures"][0] != "MPTForCausalLM":
+    print("Model architecture not supported: " + hparams["architectures"][0])
+
+    sys.exit()
+
+# get number of model parts
+num_parts = count_model_parts(dir_model)
+
+ARCH=gguf.MODEL_ARCH.MPT
+gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
+
+print("gguf: get model metadata")
+
+block_count = hparams["n_layers"]
+
+gguf_writer.add_name(dir_model.name)
+gguf_writer.add_context_length(hparams["max_seq_len"])
+gguf_writer.add_embedding_length(hparams["d_model"])
+gguf_writer.add_block_count(block_count)
+gguf_writer.add_feed_forward_length(4 * hparams["d_model"])
+gguf_writer.add_head_count(hparams["n_heads"])
+if kv_n_heads := hparams["attn_config"].get("kv_n_heads"):
+    gguf_writer.add_head_count_kv(kv_n_heads)
+gguf_writer.add_layer_norm_eps(1e-05)
+if hparams["attn_config"]["clip_qkv"] is not None:
+    gguf_writer.add_clamp_kqv(hparams["attn_config"]["clip_qkv"])
+gguf_writer.add_max_alibi_bias(hparams["attn_config"]["alibi_bias_max"])
+
+# TOKENIZATION
+
+print("gguf: get tokenizer metadata")
+
+tokens: list[bytearray] = []
+scores: list[float] = []
+toktypes: list[int] = []
+
+# gpt2 tokenizer
+gguf_writer.add_tokenizer_model("gpt2")
+
+print("gguf: get gpt2 tokenizer vocab")
+
+# MPT token embedding tensors have dimension 50432 (hparams["vocab_size"]), but
+# there are only 50254 (len(tokenizer.vocab)) tokens in the vocab, presumably to
+# accomodate some "reserved" tokens; this is causing problems down the line in
+# llama.cpp, so we pad the vocab with dummy tokens:
+
+vocab_size = hparams["vocab_size"]
+
+# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
+tokenizer = AutoTokenizer.from_pretrained(dir_model)
+
+added_vocab = tokenizer.get_added_vocab()
+reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
+
+for i in range(vocab_size):
+    if i not in reverse_vocab:
+        tokens.append(f"[PAD{i}]")
+        toktypes.append(gguf.TokenType.USER_DEFINED)
+    elif reverse_vocab[i] in added_vocab:
+        tokens.append(reverse_vocab[i])
+        if tokenizer.added_tokens_decoder[i].special:
+            toktypes.append(gguf.TokenType.CONTROL)
+        else:
+            toktypes.append(gguf.TokenType.USER_DEFINED)
+    else:
+        tokens.append(reverse_vocab[i])
+        toktypes.append(gguf.TokenType.NORMAL)
+
+gguf_writer.add_token_list(tokens)
+gguf_writer.add_token_types(toktypes)
+
+special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens))
+special_vocab.add_to_gguf(gguf_writer)
+
+# TENSORS
+
+tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
+
+# tensor info
+print("gguf: get tensor metadata")
+
+if num_parts == 0:
+    part_names = iter(("pytorch_model.bin",))
+else:
+    part_names = (
+        f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
+    )
+
+for part_name in part_names:
+    if args.vocab_only:
+        break
+    print("gguf: loading model part '" + part_name + "'")
+    model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
+
+    for name in model_part.keys():
+        data = model_part[name]
+
+        old_dtype = data.dtype
+
+        # convert any unsupported data types to float32
+        if data.dtype != torch.float16 and data.dtype != torch.float32:
+            data = data.to(torch.float32)
+
+        data = data.squeeze().numpy()
+
+        # map tensor names
+        new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
+        if new_name is None:
+            print("Cannot map tensor '" + name + "'")
+            continue # for the sake of compatibility with some old published models, don't quit
+            sys.exit()
+
+        n_dims = len(data.shape)
+        data_dtype = data.dtype
+
+        # if f32 desired, convert any float16 to float32
+        if ftype == 0 and data_dtype == np.float16:
+            data = data.astype(np.float32)
+
+        # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
+        if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
+            data = data.astype(np.float32)
+
+        # if f16 desired, convert any float32 2-dim weight tensors to float16
+        if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
+            data = data.astype(np.float16)
+
+        print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
+
+        gguf_writer.add_tensor(new_name, data)
+
+        # note: MPT output is tied to (same as) wte in original model;
+        # for easier implementation in llama.cpp it's duplicated in GGUF, though :/
+        if new_name == "token_embd.weight":
+            gguf_writer.add_tensor("output.weight", data)
+
+print("gguf: write header")
+gguf_writer.write_header_to_file()
+print("gguf: write metadata")
+gguf_writer.write_kv_data_to_file()
+if not args.vocab_only:
+    print("gguf: write tensors")
+    gguf_writer.write_tensors_to_file()
+
+gguf_writer.close()
+
+print(f"gguf: model successfully exported to '{fname_out}'")
+print("")

convert-refact-hf-to-gguf.py

@@ -139,18 +139,27 @@ tokenizer = AutoTokenizer.from_pretrained(dir_model)
 vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
 assert max(tokenizer.vocab.values()) < vocab_size
 
+added_vocab = tokenizer.get_added_vocab()
 reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
 
 for i in range(vocab_size):
-    tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]")
+    if i not in reverse_vocab:
-    scores.append(0.0) # dummy
+        tokens.append(f"[PAD{i}]")
-    toktypes.append(gguf.TokenType.NORMAL)
+        toktypes.append(gguf.TokenType.USER_DEFINED)
+    elif reverse_vocab[i] in added_vocab:
+        tokens.append(reverse_vocab[i])
+        if tokenizer.added_tokens_decoder[i].special:
+            toktypes.append(gguf.TokenType.CONTROL)
+        else:
+            toktypes.append(gguf.TokenType.USER_DEFINED)
+    else:
+        tokens.append(reverse_vocab[i])
+        toktypes.append(gguf.TokenType.NORMAL)
 
 gguf_writer.add_token_list(tokens)
-gguf_writer.add_token_scores(scores)
 gguf_writer.add_token_types(toktypes)
 
-special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
+special_vocab = gguf.SpecialVocab(dir_model, load_merges=True, n_vocab = len(tokens))
 special_vocab.add_to_gguf(gguf_writer)
 
 # TENSORS

convert-starcoder-hf-to-gguf.py

@@ -111,18 +111,26 @@ tokenizer = AutoTokenizer.from_pretrained(dir_model)
 vocab_size = hparams.get("vocab_size", len(tokenizer.vocab))
 assert max(tokenizer.vocab.values()) < vocab_size
 
+added_vocab = tokenizer.get_added_vocab()
 reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
 
 for i in range(vocab_size):
-    tokens.append(reverse_vocab[i] if i in reverse_vocab else f"[PAD{i}]")
+    if i not in reverse_vocab:
-    scores.append(0.0) # dummy
+        tokens.append(f"[PAD{i}]")
-    toktypes.append(gguf.TokenType.NORMAL)
+        toktypes.append(gguf.TokenType.USER_DEFINED)
+    elif reverse_vocab[i] in added_vocab:
+        tokens.append(reverse_vocab[i])
+        if tokenizer.added_tokens_decoder[i].special:
+            toktypes.append(gguf.TokenType.CONTROL)
+        else:
+            toktypes.append(gguf.TokenType.USER_DEFINED)
+    else:
+        tokens.append(reverse_vocab[i])
+        toktypes.append(gguf.TokenType.NORMAL)
 
 gguf_writer.add_token_list(tokens)
-gguf_writer.add_token_scores(scores)
 gguf_writer.add_token_types(toktypes)
-
+special_vocab = gguf.SpecialVocab(dir_model, load_merges = True, n_vocab = len(tokens))
-special_vocab = gguf.SpecialVocab(dir_model, load_merges = True)
 special_vocab.add_to_gguf(gguf_writer)
 
 # TENSORS

convert.py

@@ -369,7 +369,7 @@ class SentencePieceVocab:
         expected_ids = list(range(vocab_size, vocab_size + len(added_tokens)))
         actual_ids   = sorted(added_tokens.values())
         if expected_ids != actual_ids:
-            raise Exception(f"Expected added token IDs to be sequential and start at {len(added_tokens)}; got {actual_ids}")
+            raise Exception(f"Expected added token IDs to be sequential and start at {vocab_size}; got {actual_ids}")
 
         items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1])
         self.added_tokens_list = [text for (text, idx) in items]
@@ -803,8 +803,8 @@ def check_vocab_size(params: Params, vocab: Vocab) -> None:
 
 
 class OutputFile:
-    def __init__(self, fname_out: Path) -> None:
+    def __init__(self, fname_out: Path, endianess:gguf.GGUFEndian=gguf.GGUFEndian.LITTLE) -> None:
-        self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
+        self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess)
 
     def add_meta_arch(self, params: Params) -> None:
         name = "LLaMA"
@@ -875,10 +875,10 @@ class OutputFile:
         self.gguf.close()
 
     @staticmethod
-    def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab) -> None:
+    def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab, endianess:gguf.GGUFEndian=gguf.GGUFEndian.LITTLE) -> None:
         check_vocab_size(params, vocab)
 
-        of = OutputFile(fname_out)
+        of = OutputFile(fname_out, endianess=endianess)
 
         # meta data
         of.add_meta_arch(params)
@@ -903,10 +903,10 @@ class OutputFile:
         return dt.quantize(arr)
 
     @staticmethod
-    def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY) -> None:
+    def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess=gguf.GGUFEndian.LITTLE) -> None:
         check_vocab_size(params, vocab)
 
-        of = OutputFile(fname_out)
+        of = OutputFile(fname_out, endianess=endianess)
 
         # meta data
         of.add_meta_arch(params)
@@ -1123,8 +1123,9 @@ def main(args_in: list[str] | None = None) -> None:
     parser.add_argument("--vocabtype",   choices=["spm", "bpe"], help="vocab format (default: spm)", default="spm")
     parser.add_argument("--ctx",         type=int,               help="model training context (default: based on input)")
     parser.add_argument("--concurrency", type=int,               help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default = DEFAULT_CONCURRENCY)
-    args = parser.parse_args(args_in)
+    parser.add_argument("--bigendian",   action="store_true",    help="model is executed on big endian machine")
 
+    args = parser.parse_args(args_in)
     if args.dump_single:
         model_plus = lazy_load_file(args.model)
         do_dump_model(model_plus)
@@ -1138,6 +1139,9 @@ def main(args_in: list[str] | None = None) -> None:
     if args.dump:
         do_dump_model(model_plus)
         return
+    endianess = gguf.GGUFEndian.LITTLE
+    if args.bigendian:
+        endianess = gguf.GGUFEndian.BIG
 
     params = Params.load(model_plus)
     if params.n_ctx == -1:
@@ -1159,10 +1163,13 @@ def main(args_in: list[str] | None = None) -> None:
 
     vocab: Vocab
     if args.vocab_only:
-        assert args.outfile, "need --outfile if using --vocab-only"
+        if not args.outfile:
+            raise ValueError("need --outfile if using --vocab-only")
         # FIXME: Try to respect vocab_dir somehow?
         vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype)
-        special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent, load_merges = args.vocabtype == 'bpe')
+        special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,
+            load_merges = args.vocabtype == 'bpe',
+            n_vocab = vocab.vocab_size)
         outfile = args.outfile
         OutputFile.write_vocab_only(outfile, params, vocab, special_vocab)
         print(f"Wrote {outfile}")
@@ -1174,7 +1181,9 @@ def main(args_in: list[str] | None = None) -> None:
         vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent
         vocab = load_vocab(vocab_dir, args.vocabtype)
     # FIXME: Try to respect vocab_dir somehow?
-    special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent, load_merges = args.vocabtype == 'bpe')
+    special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent,
+        load_merges = args.vocabtype == 'bpe',
+        n_vocab = vocab.vocab_size)
 
     model   = model_plus.model
     model   = convert_model_names(model, params)
@@ -1185,7 +1194,7 @@ def main(args_in: list[str] | None = None) -> None:
     params.ftype = ftype
     print(f"Writing {outfile}, format {ftype}")
 
-    OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, concurrency = args.concurrency)
+    OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, concurrency = args.concurrency, endianess=endianess)
     print(f"Wrote {outfile}")
 
 

docs/BLIS.md

@@ -49,7 +49,7 @@ According to the BLIS documentation, we could set the following
 environment variables to modify the behavior of openmp:
 
 ```bash
-export GOMP_GPU_AFFINITY="0-19"
+export GOMP_CPU_AFFINITY="0-19"
 export BLIS_NUM_THREADS=14
 ```
 

examples/CMakeLists.txt

@@ -12,24 +12,26 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR})
 
 if (EMSCRIPTEN)
 else()
+    add_subdirectory(baby-llama)
+    add_subdirectory(batched)
+    add_subdirectory(batched-bench)
+    add_subdirectory(beam-search)
+    add_subdirectory(benchmark)
+    add_subdirectory(convert-llama2c-to-ggml)
+    add_subdirectory(embedding)
+    add_subdirectory(finetune)
+    add_subdirectory(infill)
+    add_subdirectory(llama-bench)
+    add_subdirectory(llava)
     add_subdirectory(main)
+    add_subdirectory(parallel)
+    add_subdirectory(perplexity)
     add_subdirectory(quantize)
     add_subdirectory(quantize-stats)
-    add_subdirectory(perplexity)
-    add_subdirectory(embedding)
     add_subdirectory(save-load-state)
-    add_subdirectory(benchmark)
-    add_subdirectory(baby-llama)
-    add_subdirectory(train-text-from-scratch)
-    add_subdirectory(finetune)
-    add_subdirectory(convert-llama2c-to-ggml)
     add_subdirectory(simple)
-    add_subdirectory(batched)
     add_subdirectory(speculative)
-    add_subdirectory(parallel)
+    add_subdirectory(train-text-from-scratch)
-    add_subdirectory(embd-input)
-    add_subdirectory(llama-bench)
-    add_subdirectory(beam-search)
     if (LLAMA_METAL)
         add_subdirectory(metal)
     endif()

examples/batched-bench/CMakeLists.txt

@@ -0,0 +1,5 @@
+set(TARGET batched-bench)
+add_executable(${TARGET} batched-bench.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)

examples/batched-bench/README.md

@@ -0,0 +1,51 @@
+# llama.cpp/example/batched-bench
+
+Benchmark the batched decoding performance of `llama.cpp`
+
+## Usage
+
+There are 2 modes of operation:
+
+- `prompt not shared` - each batch has a separate prompt of size `PP` (i.e. `N_KV = B*(PP + TG)`)
+- `prompt is shared` - there is a common prompt of size `PP` used by all batches (i.e. `N_KV = PP + B*TG`)
+
+```bash
+./batched-bench MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] <PP> <TG> <PL>
+
+# LLaMA 7B, F16, N_KV_MAX = 16384 (8GB), prompt not shared
+./batched-bench ./models/llama-7b/ggml-model-f16.gguf 16384 0 99
+
+# LLaMA 7B, Q8_0, N_KV_MAX = 16384 (8GB), prompt is shared
+./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 16384 1 99
+
+# custom set of batches
+./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32
+```
+
+## Sample results
+
+- `PP` - prompt tokens per batch
+- `TG` - generated tokens per batch
+- `B` - number of batches
+- `N_KV` - required KV cache size
+- `T_PP` - prompt processing time (i.e. time to first token)
+- `S_PP` - prompt processing speed (`(B*PP)/T_PP` or `PP/T_PP`)
+- `T_TG` - time to generate all batches
+- `S_TG` - text generation speed (`(B*TG)/T_TG`)
+- `T` - total time
+- `S` - total speed (i.e. all tokens / total time)
+
+|    PP |     TG |    B |   N_KV |   T_PP s | S_PP t/s |   T_TG s | S_TG t/s |      T s |    S t/s |
+|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
+|   128 |    128 |    1 |    256 |    0.108 |  1186.64 |    3.079 |    41.57 |    3.187 |    80.32 |
+|   128 |    128 |    2 |    512 |    0.198 |  1295.19 |    5.029 |    50.90 |    5.227 |    97.95 |
+|   128 |    128 |    4 |   1024 |    0.373 |  1373.96 |    6.878 |    74.44 |    7.251 |   141.23 |
+|   128 |    128 |    8 |   2048 |    0.751 |  1363.27 |    7.344 |   139.43 |    8.095 |   252.99 |
+|   128 |    128 |   16 |   4096 |    1.570 |  1304.68 |    8.455 |   242.23 |   10.024 |   408.60 |
+|   128 |    128 |   32 |   8192 |    3.408 |  1201.73 |    8.801 |   465.40 |   12.209 |   670.96 |
+|   128 |    256 |    1 |    384 |    0.107 |  1196.70 |    6.329 |    40.45 |    6.436 |    59.67 |
+|   128 |    256 |    2 |    768 |    0.194 |  1317.45 |   10.239 |    50.00 |   10.433 |    73.61 |
+|   128 |    256 |    4 |   1536 |    0.366 |  1399.03 |   13.960 |    73.35 |   14.326 |   107.22 |
+|   128 |    256 |    8 |   3072 |    0.751 |  1363.92 |   15.110 |   135.54 |   15.861 |   193.69 |
+|   128 |    256 |   16 |   6144 |    1.569 |  1304.93 |   18.073 |   226.64 |   19.642 |   312.80 |
+|   128 |    256 |   32 |  12288 |    3.409 |  1201.35 |   19.223 |   426.15 |   22.633 |   542.93 |

examples/batched-bench/batched-bench.cpp

@@ -0,0 +1,243 @@
+#include "common.h"
+#include "llama.h"
+
+#include <algorithm>
+#include <cmath>
+#include <cstdio>
+#include <string>
+#include <vector>
+
+// mutates the input string
+static std::vector<int> parse_list(char * p) {
+    std::vector<int> ret;
+
+    char * q = p;
+
+    while (*p) {
+        if (*p == ',') {
+            *p = '\0';
+            ret.push_back(std::atoi(q));
+            q = p + 1;
+        }
+
+        ++p;
+    }
+
+    ret.push_back(std::atoi(q));
+
+    return ret;
+}
+
+int main(int argc, char ** argv) {
+    gpt_params params;
+
+    if (argc == 1 || argv[1][0] == '-') {
+        printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] <PP> <TG> <PL>\n" , argv[0]);
+        printf("  <PP>, <TG> and PL are comma-separated lists of numbers without spaces\n\n");
+        printf("  example: %s ggml-model-f16.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
+        return 1 ;
+    }
+
+    int n_kv_max     = 2048;
+    int is_pp_shared = 0;
+    int n_gpu_layers = 0;
+    int mmq          = 0;
+
+    std::vector<int> n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, };
+    std::vector<int> n_tg = { 128, 256, };
+    std::vector<int> n_pl = { 1, 2, 4, 8, 16, 32, };
+    //std::vector<int> n_pl = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 32, };
+
+    if (argc >= 2) {
+        params.model = argv[1];
+    }
+
+    if (argc >= 3) {
+        n_kv_max = std::atoi(argv[2]);
+    }
+
+    if (argc >= 4) {
+        is_pp_shared = std::atoi(argv[3]);
+    }
+
+    if (argc >= 5) {
+        n_gpu_layers = std::atoi(argv[4]);
+    }
+
+    if (argc >= 6) {
+        mmq = std::atoi(argv[5]);
+    }
+
+    if (argc >= 7) {
+        n_pp = parse_list(argv[6]);
+    }
+
+    if (argc >= 8) {
+        n_tg = parse_list(argv[7]);
+    }
+
+    if (argc >= 9) {
+        n_pl = parse_list(argv[8]);
+    }
+
+    // init LLM
+
+    llama_backend_init(params.numa);
+
+    // initialize the model
+
+    llama_model_params model_params = llama_model_default_params();
+
+    model_params.n_gpu_layers = n_gpu_layers;
+
+    llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
+
+    if (model == NULL) {
+        fprintf(stderr , "%s: error: unable to load model\n" , __func__);
+        return 1;
+    }
+
+    llama_context_params ctx_params = llama_context_default_params();
+
+    ctx_params.seed      = 1234;
+    ctx_params.n_ctx     = n_kv_max;
+    ctx_params.n_batch   = 512;
+    ctx_params.mul_mat_q = mmq;
+
+    ctx_params.n_threads       = params.n_threads;
+    ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
+
+    llama_context * ctx = llama_new_context_with_model(model, ctx_params);
+
+    if (ctx == NULL) {
+        fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
+        return 1;
+    }
+
+    llama_batch batch = llama_batch_init(n_kv_max, 0, 1);
+
+    // decode in batches of ctx_params.n_batch tokens
+    auto decode_helper = [](llama_context * ctx, llama_batch & batch, int32_t n_batch) {
+        for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) {
+            const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i));
+
+            llama_batch batch_view = {
+                n_tokens,
+                batch.token    + i,
+                nullptr,
+                batch.pos      + i,
+                batch.n_seq_id + i,
+                batch.seq_id   + i,
+                batch.logits   + i,
+                0, 0, 0, // unused
+            };
+
+            const int ret = llama_decode(ctx, batch_view);
+            if (ret != 0) {
+                LOG_TEE("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret);
+                return false;
+            }
+        }
+
+        return true;
+    };
+
+    // warm up
+    {
+        for (int i = 0; i < 16; ++i) {
+            llama_batch_add(batch, 0, i, { 0 }, false);
+        }
+
+        if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
+            LOG_TEE("%s: llama_decode() failed\n", __func__);
+            return 1;
+        }
+    }
+
+    LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP",     "TG",     "B",    "N_KV",     "T_PP s",   "S_PP t/s", "T_TG s",   "S_TG t/s", "T s",      "S t/s");
+    LOG_TEE("|%6s-|-%6s-|-%4s-|-%6s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|-%8s-|\n", "------", "------", "----", "------", "--------", "--------", "--------", "--------", "--------", "--------");
+
+    for (        int i_pp = 0; i_pp < (int) n_pp.size(); ++i_pp) {
+        for (    int i_tg = 0; i_tg < (int) n_tg.size(); ++i_tg) {
+            for (int i_pl = 0; i_pl < (int) n_pl.size(); ++i_pl) {
+                const int pp = n_pp[i_pp];
+                const int tg = n_tg[i_tg];
+                const int pl = n_pl[i_pl];
+
+                const int n_ctx_req = is_pp_shared ? pp + pl*tg : pl*(pp + tg);
+
+                if (n_ctx_req > n_kv_max) {
+                    continue;
+                }
+
+                llama_batch_clear(batch);
+
+                const int n_tokens = is_pp_shared ? pp : pl*pp;
+
+                for (int i = 0; i < n_tokens; ++i) {
+                    llama_batch_add(batch, 0, i, { 0 }, false);
+                }
+                batch.logits[batch.n_tokens - 1] = true;
+
+                const auto t_pp_start = ggml_time_us();
+
+                llama_kv_cache_tokens_rm(ctx, -1, -1);
+
+                if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
+                    LOG_TEE("%s: llama_decode() failed\n", __func__);
+                    return 1;
+                }
+
+                if (is_pp_shared) {
+                    for (int32_t i = 1; i < pl; ++i) {
+                        llama_kv_cache_seq_cp(ctx, 0, i, 0, pp);
+                    }
+                }
+
+                const auto t_pp_end = ggml_time_us();
+
+                const auto t_tg_start = ggml_time_us();
+
+                for (int i = 0; i < tg; ++i) {
+                    llama_batch_clear(batch);
+
+                    for (int j = 0; j < pl; ++j) {
+                        llama_batch_add(batch, 0, pp + i, { j }, true);
+                    }
+
+                    if (!decode_helper(ctx, batch, ctx_params.n_batch)) {
+                        LOG_TEE("%s: llama_decode() failed\n", __func__);
+                        return 1;
+                    }
+                }
+
+                const auto t_tg_end = ggml_time_us();
+
+                const int32_t n_kv = n_ctx_req;
+
+                const float t_pp = (t_pp_end - t_pp_start) / 1000000.0f;
+                const float t_tg = (t_tg_end - t_tg_start) / 1000000.0f;
+                const float t    = t_pp + t_tg;
+
+                const float speed_pp = is_pp_shared ? pp / t_pp : pl*pp / t_pp;
+                const float speed_tg = pl*tg / t_tg;
+                const float speed    = n_kv / t;
+
+                LOG_TEE("|%6d | %6d | %4d | %6d | %8.3f | %8.2f | %8.3f | %8.2f | %8.3f | %8.2f |\n", pp, tg, pl, n_kv, t_pp, speed_pp, t_tg, speed_tg, t, speed);
+            }
+        }
+    }
+
+    llama_print_timings(ctx);
+
+    llama_batch_free(batch);
+
+    llama_free(ctx);
+    llama_free_model(model);
+
+    llama_backend_free();
+
+    fprintf(stderr, "\n\n");
+
+    return 0;
+}

examples/batched.swift/.gitignore

@@ -0,0 +1,9 @@
+.DS_Store
+/.build
+/Packages
+xcuserdata/
+DerivedData/
+.swiftpm/configuration/registries.json
+.swiftpm/xcode/package.xcworkspace/contents.xcworkspacedata
+.netrc
+batched_swift

examples/batched.swift/Makefile

@@ -0,0 +1,6 @@
+.PHONY: build
+
+build:
+	xcodebuild -scheme batched_swift -destination "generic/platform=macOS" -derivedDataPath build
+	rm -f ./batched_swift
+	ln -s ./build/Build/Products/Debug/batched_swift ./batched_swift

examples/batched.swift/Package.swift

@@ -0,0 +1,22 @@
+// swift-tools-version: 5.5
+// The swift-tools-version declares the minimum version of Swift required to build this package.
+
+import PackageDescription
+
+let package = Package(
+    name: "batched_swift",
+    platforms: [.macOS(.v12)],
+    dependencies: [
+        .package(name: "llama", path: "../../"),
+    ],
+    targets: [
+        // Targets are the basic building blocks of a package, defining a module or a test suite.
+        // Targets can depend on other targets in this package and products from dependencies.
+        .executableTarget(
+            name: "batched_swift",
+            dependencies: ["llama"],
+            path: "Sources",
+            linkerSettings: [.linkedFramework("Foundation"), .linkedFramework("AppKit")]
+        ),
+    ]
+)

examples/batched.swift/README.md

@@ -0,0 +1,4 @@
+This is a swift clone of `examples/batched`.
+
+$ `make`
+$ `./swift MODEL_PATH [PROMPT] [PARALLEL]`

examples/batched.swift/Sources/main.swift

@@ -0,0 +1,263 @@
+import Foundation
+import llama
+
+let arguments = CommandLine.arguments
+
+// Check that we have at least one argument (the model path)
+guard arguments.count > 1 else {
+    print("Usage: swift MODEL_PATH [PROMPT] [PARALLEL]")
+    exit(1)
+}
+
+let modelPath: String = arguments[1]
+let prompt: String = arguments.count > 2 ? arguments[2] : "Hello my name is"
+let n_parallel: Int = arguments.count > 3 && Int(arguments[3]) != nil ? Int(arguments[3])! : 1
+
+// total length of the sequences including the prompt
+let n_len: Int = 32
+
+// init LLM
+llama_backend_init(false)
+defer {
+    llama_backend_free()
+}
+
+let model_params = llama_model_default_params()
+guard let model = llama_load_model_from_file(modelPath.cString(using: .utf8), model_params) else {
+    print("Failed to load model")
+    exit(1)
+}
+
+defer {
+    llama_free_model(model)
+}
+
+var tokens = tokenize(text: prompt, add_bos: true)
+
+let n_kv_req = UInt32(tokens.count) + UInt32((n_len - Int(tokens.count)) * n_parallel)
+
+var context_params = llama_context_default_params()
+context_params.seed = 1234
+context_params.n_ctx = n_kv_req
+context_params.n_batch = UInt32(max(n_len, n_parallel))
+context_params.n_threads = 8
+context_params.n_threads_batch = 8
+
+let context = llama_new_context_with_model(model, context_params)
+guard context != nil else {
+    print("Failed to initialize context")
+    exit(1)
+}
+
+defer {
+    llama_free(context)
+}
+
+let n_ctx = llama_n_ctx(context)
+
+print("\nn_len = \(n_len), n_ctx = \(n_ctx), n_batch = \(context_params.n_batch), n_parallel = \(n_parallel), n_kv_req = \(n_kv_req)\n")
+
+if n_kv_req > n_ctx {
+    print("error: n_kv_req (%d) > n_ctx, the required KV cache size is not big enough\n", n_kv_req)
+    exit(1)
+}
+
+var buffer: [CChar] = []
+for id: llama_token in tokens {
+    print(token_to_piece(token: id, buffer: &buffer) ?? "", terminator: "")
+}
+
+print("\n")
+
+var batch = llama_batch_init(max(Int32(tokens.count), Int32(n_parallel)), 0, 1)
+defer {
+    llama_batch_free(batch)
+}
+
+// evaluate the initial prompt
+batch.n_tokens = Int32(tokens.count)
+
+for (i, token) in tokens.enumerated() {
+    batch.token[i] = token
+    batch.pos[i] = Int32(i)
+    batch.n_seq_id[i] = 1
+    // batch.seq_id[i][0] = 0
+    // TODO: is this the proper way to do this?
+    if let seq_id = batch.seq_id[i] {
+        seq_id[0] = 0
+    }
+    batch.logits[i] = 0
+}
+
+// llama_decode will output logits only for the last token of the prompt
+batch.logits[Int(batch.n_tokens) - 1] = 1
+
+if llama_decode(context, batch) != 0 {
+    print("llama_decode() failed")
+    exit(1)
+}
+
+for i in 1 ..< n_parallel {
+    llama_kv_cache_seq_cp(context, 0, Int32(i), 0, batch.n_tokens)
+}
+
+if n_parallel > 1 {
+    print("generating \(n_parallel) sequences ...\n")
+}
+
+var streams: [String] = .init(repeating: "", count: n_parallel)
+var streamBuffers: [[CChar]] = .init(repeating: [], count: n_parallel)
+var i_batch = [Int32](repeating: batch.n_tokens - 1, count: n_parallel)
+
+var n_cur = batch.n_tokens
+var n_decode = 0
+
+let t_main_start = ggml_time_us()
+
+while n_cur <= n_len {
+    // prepare the next batch
+    batch.n_tokens = 0
+
+    // sample the next token for each parallel sequence / stream
+    for i in 0 ..< n_parallel {
+        if i_batch[i] < 0 {
+            // the stream has already finished
+            continue
+        }
+
+        var n_vocab = llama_n_vocab(model)
+        var logits = llama_get_logits_ith(context, i_batch[i])
+
+        var candidates: [llama_token_data] = .init(repeating: llama_token_data(), count: Int(n_vocab))
+
+        for token_id in 0 ..< n_vocab {
+            candidates.append(llama_token_data(id: token_id, logit: logits![Int(token_id)], p: 0.0))
+        }
+
+        var candidates_p: llama_token_data_array = .init(
+            data: &candidates,
+            size: candidates.count,
+            sorted: false
+        )
+
+        let top_k: Int32 = 40
+        let top_p: Float = 0.9
+        let temp: Float = 0.4
+
+        llama_sample_top_k(context, &candidates_p, top_k, 1)
+        llama_sample_top_p(context, &candidates_p, top_p, 1)
+        llama_sample_temp(context, &candidates_p, temp)
+
+        let new_token_id = llama_sample_token(context, &candidates_p)
+
+        // const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
+
+        // is it an end of stream? -> mark the stream as finished
+        if new_token_id == llama_token_eos(context) || n_cur == n_len {
+            i_batch[i] = -1
+            // print("")
+            if n_parallel > 1 {
+                print("stream \(i) finished at n_cur = \(n_cur)")
+            }
+
+            continue
+        }
+
+        let nextStringPiece = token_to_piece(token: new_token_id, buffer: &streamBuffers[i]) ?? ""
+
+        // if there is only one stream, we print immediately to stdout
+        if n_parallel == 1 {
+            print(nextStringPiece, terminator: "")
+        }
+        streams[i] += nextStringPiece
+
+        // push this new token for next evaluation
+        batch.token[Int(batch.n_tokens)] = new_token_id
+        batch.pos[Int(batch.n_tokens)] = n_cur
+        batch.n_seq_id[Int(batch.n_tokens)] = 1
+        if let seq_id = batch.seq_id[Int(batch.n_tokens)] {
+            seq_id[0] = Int32(i)
+        }
+        batch.logits[Int(batch.n_tokens)] = 1
+
+        i_batch[i] = batch.n_tokens
+
+        batch.n_tokens += 1
+
+        n_decode += 1
+    }
+
+    // all streams are finished
+    if batch.n_tokens == 0 {
+        break
+    }
+
+    n_cur += 1
+
+    // evaluate the current batch with the transformer model
+    if llama_decode(context, batch) != 0 {
+        print("llama_decode() failed")
+        exit(1)
+    }
+}
+
+if n_parallel > 1 {
+    print("\n")
+    for (i, stream) in streams.enumerated() {
+        print("sequence \(i):\n\n\(prompt)\(stream)\n")
+    }
+}
+
+let t_main_end = ggml_time_us()
+
+print("decoded \(n_decode) tokens in \(String(format: "%.2f", Double(t_main_end - t_main_start) / 1_000_000.0)) s, speed: \(String(format: "%.2f", Double(n_decode) / (Double(t_main_end - t_main_start) / 1_000_000.0))) t/s\n")
+
+llama_print_timings(context)
+
+private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
+    let n_tokens = text.count + (add_bos ? 1 : 0)
+    let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
+    let tokenCount = llama_tokenize(model, text, Int32(text.count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false)
+    var swiftTokens: [llama_token] = []
+    for i in 0 ..< tokenCount {
+        swiftTokens.append(tokens[Int(i)])
+    }
+    tokens.deallocate()
+    return swiftTokens
+}
+
+private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String? {
+    var result = [CChar](repeating: 0, count: 8)
+    let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count))
+    if nTokens < 0 {
+        if result.count >= -Int(nTokens) {
+            result.removeLast(-Int(nTokens))
+        } else {
+            result.removeAll()
+        }
+        let check = llama_token_to_piece(
+            model,
+            token,
+            &result,
+            Int32(result.count)
+        )
+        assert(check == nTokens)
+    } else {
+        result.removeLast(result.count - Int(nTokens))
+    }
+    if buffer.isEmpty, let utfString = String(cString: result + [0], encoding: .utf8) {
+        return utfString
+    } else {
+        buffer.append(contentsOf: result)
+        let data = Data(buffer.map { UInt8(bitPattern: $0) })
+        if buffer.count >= 4 { // 4 bytes is the max length of a utf8 character so if we're here we need to reset the buffer
+            buffer = []
+        }
+        guard let bufferString = String(data: data, encoding: .utf8) else {
+            return nil
+        }
+        buffer = []
+        return bufferString
+    }
+    return nil
+}

examples/batched/batched.cpp

@@ -11,12 +11,16 @@ int main(int argc, char ** argv) {
     gpt_params params;
 
     if (argc == 1 || argv[1][0] == '-') {
-        printf("usage: %s MODEL_PATH [PROMPT] [PARALLEL]\n" , argv[0]);
+        printf("usage: %s MODEL_PATH [PROMPT] [PARALLEL] [LEN]\n" , argv[0]);
         return 1 ;
     }
 
+    // number of parallel batches
     int n_parallel = 1;
 
+    // total length of the sequences including the prompt
+    int n_len = 32;
+
     if (argc >= 2) {
         params.model = argv[1];
     }
@@ -29,13 +33,14 @@ int main(int argc, char ** argv) {
         n_parallel = std::atoi(argv[3]);
     }
 
+    if (argc >= 5) {
+        n_len = std::atoi(argv[4]);
+    }
+
     if (params.prompt.empty()) {
         params.prompt = "Hello my name is";
     }
 
-    // total length of the sequences including the prompt
-    const int n_len = 32;
-
     // init LLM
 
     llama_backend_init(params.numa);
@@ -66,7 +71,7 @@ int main(int argc, char ** argv) {
     ctx_params.seed  = 1234;
     ctx_params.n_ctx = n_kv_req;
     ctx_params.n_batch = std::max(n_len, n_parallel);
-    ctx_params.n_threads = params.n_threads;
+    ctx_params.n_threads       = params.n_threads;
     ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
 
     llama_context * ctx = llama_new_context_with_model(model, ctx_params);
@@ -97,20 +102,15 @@ int main(int argc, char ** argv) {
 
     fflush(stderr);
 
-    // create a llama_batch with size 512
+    // create a llama_batch
     // we use this object to submit token data for decoding
-
+    llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t)n_parallel), 0, 1);
-    llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t)n_parallel), 0);
 
     // evaluate the initial prompt
-    batch.n_tokens = tokens_list.size();
+    for (size_t i = 0; i < tokens_list.size(); ++i) {
-
+        llama_batch_add(batch, tokens_list[i], i, { 0 }, false);
-    for (int32_t i = 0; i < batch.n_tokens; i++) {
-        batch.token[i]  = tokens_list[i];
-        batch.pos[i]    = i;
-        batch.seq_id[i] = 0;
-        batch.logits[i] = false;
     }
+    GGML_ASSERT(batch.n_tokens == (int) tokens_list.size());
 
     // llama_decode will output logits only for the last token of the prompt
     batch.logits[batch.n_tokens - 1] = true;
@@ -146,7 +146,7 @@ int main(int argc, char ** argv) {
 
     while (n_cur <= n_len) {
         // prepare the next batch
-        batch.n_tokens = 0;
+        llama_batch_clear(batch);
 
         // sample the next token for each parallel sequence / stream
         for (int32_t i = 0; i < n_parallel; ++i) {
@@ -180,7 +180,7 @@ int main(int argc, char ** argv) {
             //const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
 
             // is it an end of stream? -> mark the stream as finished
-            if (new_token_id == llama_token_eos(ctx) || n_cur == n_len) {
+            if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
                 i_batch[i] = -1;
                 LOG_TEE("\n");
                 if (n_parallel > 1) {
@@ -198,15 +198,10 @@ int main(int argc, char ** argv) {
 
             streams[i] += llama_token_to_piece(ctx, new_token_id);
 
-            // push this new token for next evaluation
-            batch.token [batch.n_tokens] = new_token_id;
-            batch.pos   [batch.n_tokens] = n_cur;
-            batch.seq_id[batch.n_tokens] = i;
-            batch.logits[batch.n_tokens] = true;
-
             i_batch[i] = batch.n_tokens;
 
-            batch.n_tokens += 1;
+            // push this new token for next evaluation
+            llama_batch_add(batch, new_token_id, n_cur, { i }, true);
 
             n_decode += 1;
         }

examples/beam-search/beam-search.cpp

@@ -47,7 +47,7 @@ struct beam_search_callback_data {
 // In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same.
 // For example, eob can be flagged due to maximum token length, stop words, etc.
 static bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) {
-    return n_tokens && tokens[n_tokens-1] == llama_token_eos(callback_data.ctx);
+    return n_tokens && tokens[n_tokens-1] == llama_token_eos(llama_get_model(callback_data.ctx));
 }
 
 // Function matching type llama_beam_search_callback_fn_t.

examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp

@@ -536,7 +536,7 @@ static bool is_ggml_file(const char * filename) {
     if (file.size < 4) {
         return false;
     }
-    uint32_t magic = file.read_u32();
+    std::string magic = file.read_string(4);
     return magic == GGUF_MAGIC;
 }
 

examples/embd-input/.gitignore

@@ -1,4 +0,0 @@
-PandaGPT
-MiniGPT-4
-*.pth
-

examples/embd-input/CMakeLists.txt

@@ -1,17 +0,0 @@
-set(TARGET embdinput)
-add_library(${TARGET} embd-input-lib.cpp embd-input.h)
-install(TARGETS ${TARGET} LIBRARY)
-target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_11)
-if(TARGET BUILD_INFO)
-  add_dependencies(${TARGET} BUILD_INFO)
-endif()
-
-set(TARGET embd-input-test)
-add_executable(${TARGET} embd-input-test.cpp)
-install(TARGETS ${TARGET} RUNTIME)
-target_link_libraries(${TARGET} PRIVATE common llama embdinput ${CMAKE_THREAD_LIBS_INIT})
-target_compile_features(${TARGET} PRIVATE cxx_std_11)
-if(TARGET BUILD_INFO)
-  add_dependencies(${TARGET} BUILD_INFO)
-endif()

examples/embd-input/README.md

@@ -1,63 +0,0 @@
-### Examples for input embedding directly
-
-## Requirement
-build  `libembdinput.so`
-run the following comman in main dir (../../).
-```
-make
-```
-
-## [LLaVA](https://github.com/haotian-liu/LLaVA/) example  (llava.py)
-
-1. Obtian LLaVA model (following https://github.com/haotian-liu/LLaVA/ , use https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/).
-2. Convert it to ggml format.
-3. `llava_projection.pth` is [pytorch_model-00003-of-00003.bin](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin).
-
-```
-import torch
-
-bin_path = "../LLaVA-13b-delta-v1-1/pytorch_model-00003-of-00003.bin"
-pth_path = "./examples/embd-input/llava_projection.pth"
-
-dic = torch.load(bin_path)
-used_key = ["model.mm_projector.weight","model.mm_projector.bias"]
-torch.save({k: dic[k] for k in used_key}, pth_path)
-```
-4. Check the path of LLaVA model and `llava_projection.pth` in `llava.py`.
-
-
-## [PandaGPT](https://github.com/yxuansu/PandaGPT) example (panda_gpt.py)
-
-1. Obtian PandaGPT lora model from https://github.com/yxuansu/PandaGPT. Rename the file to `adapter_model.bin`. Use [convert-lora-to-ggml.py](../../convert-lora-to-ggml.py) to convert it to ggml format.
-The `adapter_config.json` is
-```
-{
-  "peft_type": "LORA",
-  "fan_in_fan_out": false,
-  "bias": null,
-  "modules_to_save": null,
-  "r": 32,
-  "lora_alpha": 32,
-  "lora_dropout": 0.1,
-  "target_modules": ["q_proj", "k_proj", "v_proj", "o_proj"]
-}
-```
-2. Papare the `vicuna` v0 model.
-3. Obtain the [ImageBind](https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth) model.
-4. Clone the PandaGPT source.
-```
-git clone https://github.com/yxuansu/PandaGPT
-```
-5. Install the requirement of PandaGPT.
-6. Check the path of PandaGPT source, ImageBind model, lora model and vicuna model in panda_gpt.py.
-
-## [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4/) example (minigpt4.py)
-
-1. Obtain MiniGPT-4 model from https://github.com/Vision-CAIR/MiniGPT-4/ and put it in `embd-input`.
-2. Clone the MiniGPT-4 source.
-```
-git clone https://github.com/Vision-CAIR/MiniGPT-4/
-```
-3. Install the requirement of PandaGPT.
-4. Papare the `vicuna` v0 model.
-5. Check the path of MiniGPT-4 source, MiniGPT-4 model and vicuna model in `minigpt4.py`.

examples/embd-input/embd-input-lib.cpp

@@ -1,220 +0,0 @@
-#include "build-info.h"
-#include "common.h"
-#include "embd-input.h"
-
-#include <cassert>
-#include <cinttypes>
-#include <cmath>
-#include <cstdio>
-#include <cstring>
-#include <ctime>
-#include <fstream>
-#include <iostream>
-#include <string>
-#include <vector>
-
-static llama_context ** g_ctx;
-
-extern "C" {
-
-struct MyModel* create_mymodel(int argc, char ** argv) {
-    gpt_params params;
-
-    if (!gpt_params_parse(argc, argv, params)) {
-        return nullptr;
-    }
-
-    print_build_info();
-
-    if (params.seed == LLAMA_DEFAULT_SEED) {
-        params.seed = uint32_t(time(NULL));
-    }
-    fprintf(stderr, "%s: seed  = %d\n", __func__, params.seed);
-
-    llama_backend_init(params.numa);
-
-    llama_model * model;
-    llama_context * ctx;
-
-    g_ctx = &ctx;
-
-    // load the model and apply lora adapter, if any
-    std::tie(model, ctx) = llama_init_from_gpt_params(params);
-    if (model == NULL) {
-        fprintf(stderr, "%s: error: unable to load model\n", __func__);
-        return nullptr;
-    }
-
-    // print system information
-    {
-        fprintf(stderr, "\n");
-        fprintf(stderr, "%s\n", get_system_info(params).c_str());
-    }
-    struct MyModel * ret = new MyModel();
-    ret->ctx = ctx;
-    ret->params = params;
-    ret->n_past = 0;
-    // printf("ctx: %d\n", ret->ctx);
-    return ret;
-}
-
-void free_mymodel(struct MyModel * mymodel) {
-    llama_context * ctx = mymodel->ctx;
-    llama_print_timings(ctx);
-    llama_free(ctx);
-    delete mymodel;
-}
-
-
-bool eval_float(void * model, float * input, int N){
-    MyModel * mymodel = (MyModel*)model;
-    llama_context * ctx = mymodel->ctx;
-    gpt_params params = mymodel->params;
-    int n_emb = llama_n_embd(llama_get_model(ctx));
-    int n_past = mymodel->n_past;
-    int n_batch = N; // params.n_batch;
-
-    for (int i = 0; i < (int) N; i += n_batch) {
-        int n_eval = (int) N - i;
-        if (n_eval > n_batch) {
-            n_eval = n_batch;
-        }
-        llama_batch batch = {  int32_t(n_eval), nullptr, (input+i*n_emb), nullptr, nullptr, nullptr, n_past, 1, 0, };
-        if (llama_decode(ctx, batch)) {
-            fprintf(stderr, "%s : failed to eval\n", __func__);
-            return false;
-        }
-        n_past += n_eval;
-    }
-    mymodel->n_past = n_past;
-    return true;
-}
-
-bool eval_tokens(void * model, std::vector<llama_token> tokens) {
-    MyModel * mymodel = (MyModel* )model;
-    llama_context * ctx;
-    ctx = mymodel->ctx;
-    gpt_params params = mymodel->params;
-    int n_past = mymodel->n_past;
-    for (int i = 0; i < (int) tokens.size(); i += params.n_batch) {
-        int n_eval = (int) tokens.size() - i;
-        if (n_eval > params.n_batch) {
-            n_eval = params.n_batch;
-        }
-        if (llama_decode(ctx, llama_batch_get_one(&tokens[i], n_eval, n_past, 0))) {
-            fprintf(stderr, "%s : failed to eval\n", __func__);
-            return false;
-        }
-        n_past += n_eval;
-    }
-    mymodel->n_past = n_past;
-    return true;
-}
-
-bool eval_id(struct MyModel* mymodel, int id) {
-    std::vector<llama_token> tokens;
-    tokens.push_back(id);
-    return eval_tokens(mymodel, tokens);
-}
-
-bool eval_string(struct MyModel * mymodel,const char* str){
-    llama_context * ctx = mymodel->ctx;
-    std::string str2 = str;
-    std::vector<llama_token> embd_inp = ::llama_tokenize(ctx, str2, true);
-    eval_tokens(mymodel, embd_inp);
-    return true;
-}
-
-llama_token sampling_id(struct MyModel* mymodel) {
-    llama_context* ctx = mymodel->ctx;
-    gpt_params params = mymodel->params;
-    // int n_ctx = llama_n_ctx(ctx);
-
-    // out of user input, sample next token
-    const float   temp            = params.temp;
-    const int32_t top_k           = params.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx)) : params.top_k;
-    const float   top_p           = params.top_p;
-    const float   tfs_z           = params.tfs_z;
-    const float   typical_p       = params.typical_p;
-    // const int32_t repeat_last_n   = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
-    // const float   repeat_penalty  = params.repeat_penalty;
-    // const float   alpha_presence  = params.presence_penalty;
-    // const float   alpha_frequency = params.frequency_penalty;
-    const int     mirostat        = params.mirostat;
-    const float   mirostat_tau    = params.mirostat_tau;
-    const float   mirostat_eta    = params.mirostat_eta;
-    // const bool    penalize_nl     = params.penalize_nl;
-
-    llama_token id = 0;
-    {
-        auto logits  = llama_get_logits(ctx);
-        auto n_vocab = llama_n_vocab(llama_get_model(ctx));
-
-        // Apply params.logit_bias map
-        for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
-            logits[it->first] += it->second;
-        }
-
-        std::vector<llama_token_data> candidates;
-        candidates.reserve(n_vocab);
-        for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-            candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
-        }
-
-        llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
-
-        // TODO: Apply penalties
-        // float nl_logit = logits[llama_token_nl(ctx)];
-        // auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
-        // llama_sample_repetition_penalty(ctx, &candidates_p,
-        //      last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
-        //      last_n_repeat, repeat_penalty);
-        // llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
-        // last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
-        // last_n_repeat, alpha_frequency, alpha_presence);
-        // if (!penalize_nl) {
-        //     logits[llama_token_nl(ctx)] = nl_logit;
-        // }
-
-        if (temp <= 0) {
-            // Greedy sampling
-            id = llama_sample_token_greedy(ctx, &candidates_p);
-        } else {
-            if (mirostat == 1) {
-                static float mirostat_mu = 2.0f * mirostat_tau;
-                const int mirostat_m = 100;
-                llama_sample_temp(ctx, &candidates_p, temp);
-                id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
-            } else if (mirostat == 2) {
-                static float mirostat_mu = 2.0f * mirostat_tau;
-                llama_sample_temp(ctx, &candidates_p, temp);
-                id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
-            } else {
-                // Temperature sampling
-                llama_sample_top_k(ctx, &candidates_p, top_k, 1);
-                llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
-                llama_sample_typical(ctx, &candidates_p, typical_p, 1);
-                llama_sample_top_p(ctx, &candidates_p, top_p, 1);
-                llama_sample_temp(ctx, &candidates_p, temp);
-                id = llama_sample_token(ctx, &candidates_p);
-            }
-        }
-    }
-
-    return id;
-}
-
-const char * sampling(struct MyModel * mymodel) {
-    llama_context * ctx = mymodel->ctx;
-    int id = sampling_id(mymodel);
-    static std::string ret;
-    if (id == llama_token_eos(ctx)) {
-        ret = "</s>";
-    } else {
-        ret = llama_token_to_piece(ctx, id);
-    }
-    eval_id(mymodel, id);
-    return ret.c_str();
-}
-
-}

examples/embd-input/embd-input-test.cpp

@@ -1,35 +0,0 @@
-#include "embd-input.h"
-#include <stdlib.h>
-#include <random>
-#include <string.h>
-
-int main(int argc, char** argv) {
-
-    auto mymodel = create_mymodel(argc, argv);
-    int N = 10;
-    int max_tgt_len = 500;
-    int n_embd = llama_n_embd(llama_get_model(mymodel->ctx));
-
-    // add random float embd to test evaluation
-    float * data = new float[N*n_embd];
-    std::default_random_engine e;
-    std::uniform_real_distribution<float>  u(0,1);
-    for (int i=0;i<N*n_embd;i++) {
-        data[i] = u(e);
-    }
-
-    eval_string(mymodel, "user: what is the color of the flag of UN?");
-    eval_float(mymodel, data, N);
-    eval_string(mymodel, "assistant:");
-    eval_string(mymodel, mymodel->params.prompt.c_str());
-    const char* tmp;
-    for (int i=0; i<max_tgt_len; i++) {
-        tmp = sampling(mymodel);
-        if (strcmp(tmp, "</s>")==0) break;
-        printf("%s", tmp);
-        fflush(stdout);
-    }
-    printf("\n");
-    free_mymodel(mymodel);
-    return 0;
-}

examples/embd-input/embd-input.h

@@ -1,27 +0,0 @@
-#ifndef _EMBD_INPUT_H_
-#define _EMBD_INPUT_H_ 1
-
-#include "common.h"
-#include "llama.h"
-
-extern "C" {
-
-typedef struct MyModel {
-    llama_context* ctx;
-    gpt_params params;
-    int n_past = 0;
-} MyModel;
-
-struct MyModel* create_mymodel(int argc, char ** argv);
-
-bool eval_float(void* model, float* input, int N);
-bool eval_tokens(void* model, std::vector<llama_token> tokens);
-bool eval_id(struct MyModel* mymodel, int id);
-bool eval_string(struct MyModel* mymodel, const char* str);
-const char * sampling(struct MyModel* mymodel);
-llama_token sampling_id(struct MyModel* mymodel);
-void free_mymodel(struct MyModel* mymodel);
-
-}
-
-#endif

examples/embd-input/embd_input.py

@@ -1,72 +0,0 @@
-#!/usr/bin/env python3
-import ctypes
-from ctypes import cdll, c_char_p, c_void_p, POINTER, c_float, c_int
-import numpy as np
-import os
-
-libc = cdll.LoadLibrary("./libembdinput.so")
-libc.sampling.restype=c_char_p
-libc.create_mymodel.restype=c_void_p
-libc.eval_string.argtypes=[c_void_p, c_char_p]
-libc.sampling.argtypes=[c_void_p]
-libc.eval_float.argtypes=[c_void_p, POINTER(c_float), c_int]
-
-
-class MyModel:
-    def __init__(self, args):
-        argc = len(args)
-        c_str = [c_char_p(i.encode()) for i in args]
-        args_c = (c_char_p * argc)(*c_str)
-        self.model = c_void_p(libc.create_mymodel(argc, args_c))
-        self.max_tgt_len = 512
-        self.print_string_eval = True
-
-    def __del__(self):
-        libc.free_mymodel(self.model)
-
-    def eval_float(self, x):
-        libc.eval_float(self.model, x.astype(np.float32).ctypes.data_as(POINTER(c_float)), x.shape[1])
-
-    def eval_string(self, x):
-        libc.eval_string(self.model, x.encode()) # c_char_p(x.encode()))
-        if self.print_string_eval:
-            print(x)
-
-    def eval_token(self, x):
-        libc.eval_id(self.model, x)
-
-    def sampling(self):
-        s = libc.sampling(self.model)
-        return s
-
-    def stream_generate(self, end="</s>"):
-        ret = b""
-        end = end.encode()
-        for _ in range(self.max_tgt_len):
-            tmp = self.sampling()
-            ret += tmp
-            yield tmp
-            if ret.endswith(end):
-                break
-
-    def generate_with_print(self, end="</s>"):
-        ret = b""
-        for i in self.stream_generate(end=end):
-            ret += i
-            print(i.decode(errors="replace"), end="", flush=True)
-        print("")
-        return ret.decode(errors="replace")
-
-
-    def generate(self, end="</s>"):
-        text = b"".join(self.stream_generate(end=end))
-        return text.decode(errors="replace")
-
-if __name__ == "__main__":
-    model = MyModel(["main", "--model", "../llama.cpp/models/ggml-vic13b-q4_1.bin", "-c", "2048"])
-    model.eval_string("""user: what is the color of the flag of UN?""")
-    x = np.random.random((5120,10))# , dtype=np.float32)
-    model.eval_float(x)
-    model.eval_string("""assistant:""")
-    for i in model.generate():
-        print(i.decode(errors="replace"), end="", flush=True)

examples/embd-input/llava.py

@@ -1,71 +0,0 @@
-#!/usr/bin/env python3
-import sys
-import os
-sys.path.insert(0, os.path.dirname(__file__))
-from embd_input import MyModel
-import numpy as np
-from torch import nn
-import torch
-from transformers import CLIPVisionModel,  CLIPImageProcessor
-from PIL import Image
-
-# model parameters from 'liuhaotian/LLaVA-13b-delta-v1-1'
-vision_tower = "openai/clip-vit-large-patch14"
-select_hidden_state_layer = -2
-# (vision_config.image_size // vision_config.patch_size) ** 2
-image_token_len = (224//14)**2
-
-class Llava:
-    def __init__(self, args):
-        self.image_processor = CLIPImageProcessor.from_pretrained(vision_tower)
-        self.vision_tower = CLIPVisionModel.from_pretrained(vision_tower)
-        self.mm_projector = nn.Linear(1024, 5120)
-        self.model = MyModel(["main", *args])
-
-    def load_projection(self, path):
-        state = torch.load(path)
-        self.mm_projector.load_state_dict({
-            "weight": state["model.mm_projector.weight"],
-            "bias": state["model.mm_projector.bias"]})
-
-    def chat(self, question):
-        self.model.eval_string("user: ")
-        self.model.eval_string(question)
-        self.model.eval_string("\nassistant: ")
-        return self.model.generate_with_print()
-
-    def chat_with_image(self, image, question):
-        with torch.no_grad():
-            embd_image = self.image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
-            image_forward_out = self.vision_tower(embd_image.unsqueeze(0), output_hidden_states=True)
-            select_hidden_state = image_forward_out.hidden_states[select_hidden_state_layer]
-            image_feature = select_hidden_state[:, 1:]
-            embd_image = self.mm_projector(image_feature)
-            embd_image = embd_image.cpu().numpy()[0]
-        self.model.eval_string("user: ")
-        self.model.eval_token(32003-2) # im_start
-        self.model.eval_float(embd_image.T)
-        for i in range(image_token_len-embd_image.shape[0]):
-            self.model.eval_token(32003-3) # im_patch
-        self.model.eval_token(32003-1) # im_end
-        self.model.eval_string(question)
-        self.model.eval_string("\nassistant: ")
-        return self.model.generate_with_print()
-
-
-if __name__=="__main__":
-    # model form liuhaotian/LLaVA-13b-delta-v1-1
-    a = Llava(["--model", "./models/ggml-llava-13b-v1.1.bin", "-c", "2048"])
-    # Extract from https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin.
-    # Also here can use pytorch_model-00003-of-00003.bin directly.
-    a.load_projection(os.path.join(
-        os.path.dirname(__file__) ,
-        "llava_projection.pth"))
-    respose = a.chat_with_image(
-        Image.open("./media/llama1-logo.png").convert('RGB'),
-        "what is the text in the picture?")
-    respose
-    a.chat("what is the color of it?")
-
-
-

examples/embd-input/minigpt4.py

@@ -1,129 +0,0 @@
-#!/usr/bin/env python3
-import sys
-import os
-sys.path.insert(0, os.path.dirname(__file__))
-from embd_input import MyModel
-import numpy as np
-from torch import nn
-import torch
-from PIL import Image
-
-minigpt4_path = os.path.join(os.path.dirname(__file__), "MiniGPT-4")
-sys.path.insert(0, minigpt4_path)
-from minigpt4.models.blip2 import Blip2Base
-from minigpt4.processors.blip_processors import Blip2ImageEvalProcessor
-
-
-class MiniGPT4(Blip2Base):
-    """
-    MiniGPT4 model from https://github.com/Vision-CAIR/MiniGPT-4
-    """
-    def __init__(self,
-        args,
-        vit_model="eva_clip_g",
-        q_former_model="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth",
-        img_size=224,
-        drop_path_rate=0,
-        use_grad_checkpoint=False,
-        vit_precision="fp32",
-        freeze_vit=True,
-        freeze_qformer=True,
-        num_query_token=32,
-        llama_model="",
-        prompt_path="",
-        prompt_template="",
-        max_txt_len=32,
-        end_sym='\n',
-        low_resource=False,  # use 8 bit and put vit in cpu
-        device_8bit=0
-    ):
-        super().__init__()
-        self.img_size = img_size
-        self.low_resource = low_resource
-        self.preprocessor = Blip2ImageEvalProcessor(img_size)
-
-        print('Loading VIT')
-        self.visual_encoder, self.ln_vision = self.init_vision_encoder(
-            vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision
-        )
-        print('Loading VIT Done')
-        print('Loading Q-Former')
-        self.Qformer, self.query_tokens = self.init_Qformer(
-            num_query_token, self.visual_encoder.num_features
-        )
-        self.Qformer.cls = None
-        self.Qformer.bert.embeddings.word_embeddings = None
-        self.Qformer.bert.embeddings.position_embeddings = None
-        for layer in self.Qformer.bert.encoder.layer:
-            layer.output = None
-            layer.intermediate = None
-        self.load_from_pretrained(url_or_filename=q_former_model)
-        print('Loading Q-Former Done')
-        self.llama_proj = nn.Linear(
-            self.Qformer.config.hidden_size, 5120 # self.llama_model.config.hidden_size
-        )
-        self.max_txt_len = max_txt_len
-        self.end_sym = end_sym
-        self.model = MyModel(["main", *args])
-        # system prompt
-        self.model.eval_string("Give the following image: <Img>ImageContent</Img>. "
-           "You will be able to see the image once I provide it to you. Please answer my questions."
-           "###")
-
-    def encode_img(self, image):
-        image = self.preprocessor(image)
-        image = image.unsqueeze(0)
-        device = image.device
-        if self.low_resource:
-            self.vit_to_cpu()
-            image = image.to("cpu")
-
-        with self.maybe_autocast():
-            image_embeds = self.ln_vision(self.visual_encoder(image)).to(device)
-            image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
-
-            query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
-            query_output = self.Qformer.bert(
-                query_embeds=query_tokens,
-                encoder_hidden_states=image_embeds,
-                encoder_attention_mask=image_atts,
-                return_dict=True,
-            )
-
-            inputs_llama = self.llama_proj(query_output.last_hidden_state)
-            # atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image.device)
-        return inputs_llama
-
-    def load_projection(self, path):
-        state = torch.load(path)["model"]
-        self.llama_proj.load_state_dict({
-            "weight": state["llama_proj.weight"],
-            "bias": state["llama_proj.bias"]})
-
-    def chat(self, question):
-        self.model.eval_string("Human: ")
-        self.model.eval_string(question)
-        self.model.eval_string("\n### Assistant:")
-        return self.model.generate_with_print(end="###")
-
-    def chat_with_image(self, image, question):
-        with torch.no_grad():
-            embd_image = self.encode_img(image)
-        embd_image = embd_image.cpu().numpy()[0]
-        self.model.eval_string("Human: <Img>")
-        self.model.eval_float(embd_image.T)
-        self.model.eval_string("</Img> ")
-        self.model.eval_string(question)
-        self.model.eval_string("\n### Assistant:")
-        return self.model.generate_with_print(end="###")
-
-
-if __name__=="__main__":
-    a = MiniGPT4(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048"])
-    a.load_projection(os.path.join(
-        os.path.dirname(__file__) ,
-        "pretrained_minigpt4.pth"))
-    respose = a.chat_with_image(
-        Image.open("./media/llama1-logo.png").convert('RGB'),
-        "what is the text in the picture?")
-    a.chat("what is the color of it?")

examples/embd-input/panda_gpt.py

@@ -1,99 +0,0 @@
-#!/usr/bin/env python3
-import sys
-import os
-sys.path.insert(0, os.path.dirname(__file__))
-from embd_input import MyModel
-import numpy as np
-from torch import nn
-import torch
-
-# use PandaGPT path
-panda_gpt_path = os.path.join(os.path.dirname(__file__), "PandaGPT")
-imagebind_ckpt_path = "./models/panda_gpt/"
-
-sys.path.insert(0, os.path.join(panda_gpt_path,"code","model"))
-from ImageBind.models import imagebind_model
-from ImageBind import data
-
-ModalityType = imagebind_model.ModalityType
-max_tgt_len = 400
-
-class PandaGPT:
-    def __init__(self, args):
-        self.visual_encoder,_ = imagebind_model.imagebind_huge(pretrained=True, store_path=imagebind_ckpt_path)
-        self.visual_encoder.eval()
-        self.llama_proj = nn.Linear(1024, 5120) # self.visual_hidden_size, 5120)
-        self.max_tgt_len = max_tgt_len
-        self.model = MyModel(["main", *args])
-        self.generated_text = ""
-        self.device = "cpu"
-
-    def load_projection(self, path):
-        state = torch.load(path, map_location="cpu")
-        self.llama_proj.load_state_dict({
-            "weight": state["llama_proj.weight"],
-            "bias": state["llama_proj.bias"]})
-
-    def eval_inputs(self, inputs):
-        self.model.eval_string("<Img>")
-        embds = self.extract_multimoal_feature(inputs)
-        for i in embds:
-            self.model.eval_float(i.T)
-        self.model.eval_string("</Img> ")
-
-    def chat(self, question):
-        return self.chat_with_image(None, question)
-
-    def chat_with_image(self, inputs, question):
-        if self.generated_text == "":
-            self.model.eval_string("###")
-        self.model.eval_string(" Human: ")
-        if inputs:
-            self.eval_inputs(inputs)
-        self.model.eval_string(question)
-        self.model.eval_string("\n### Assistant:")
-        ret = self.model.generate_with_print(end="###")
-        self.generated_text += ret
-        return ret
-
-    def extract_multimoal_feature(self, inputs):
-        features = []
-        for key in ["image", "audio", "video", "thermal"]:
-            if key + "_paths" in inputs:
-                embeds = self.encode_data(key, inputs[key+"_paths"])
-                features.append(embeds)
-        return features
-
-    def encode_data(self, data_type, data_paths):
-
-        type_map = {
-            "image": ModalityType.VISION,
-            "audio": ModalityType.AUDIO,
-            "video": ModalityType.VISION,
-            "thermal": ModalityType.THERMAL,
-        }
-        load_map = {
-            "image": data.load_and_transform_vision_data,
-            "audio": data.load_and_transform_audio_data,
-            "video": data.load_and_transform_video_data,
-            "thermal": data.load_and_transform_thermal_data
-        }
-
-        load_function = load_map[data_type]
-        key = type_map[data_type]
-
-        inputs = {key: load_function(data_paths, self.device)}
-        with torch.no_grad():
-            embeddings = self.visual_encoder(inputs)
-            embeds = embeddings[key]
-            embeds = self.llama_proj(embeds).cpu().numpy()
-        return embeds
-
-
-if __name__=="__main__":
-    a = PandaGPT(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048", "--lora", "./models/panda_gpt/ggml-adapter-model.bin","--temp", "0"])
-    a.load_projection("./models/panda_gpt/adapter_model.bin")
-    a.chat_with_image(
-        {"image_paths": ["./media/llama1-logo.png"]},
-        "what is the text in the picture? 'llama' or 'lambda'?")
-    a.chat("what is the color of it?")

examples/finetune/finetune.cpp

@@ -529,13 +529,14 @@ static void init_lora(const struct my_llama_model * model, struct my_llama_lora
     set_param_lora(lora);
 
     // measure data size
-    struct ggml_allocr * alloc = NULL;
+    size_t size = 0;
-    alloc = ggml_allocr_new_measure(tensor_alignment);
+    for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
-    alloc_lora(alloc, lora);
+        size += GGML_PAD(ggml_nbytes(t), tensor_alignment);
+    }
 
     // allocate data
-    lora->data.resize(ggml_allocr_max_size(alloc) + tensor_alignment);
+    struct ggml_allocr * alloc = NULL;
-    ggml_allocr_free(alloc);
+    lora->data.resize(size + tensor_alignment);
     alloc = ggml_allocr_new(lora->data.data(), lora->data.size(), tensor_alignment);
     alloc_lora(alloc, lora);
     ggml_allocr_free(alloc);
@@ -1714,11 +1715,9 @@ int main(int argc, char ** argv) {
     struct ggml_tensor * target_probs  = ggml_new_tensor_3d(ctx_input, GGML_TYPE_F32, n_vocab,  n_tokens, n_batch);
 
     // measure required memory for input tensors
-    alloc = ggml_allocr_new_measure(tensor_alignment);
+    size_t max_input_size = GGML_PAD(ggml_nbytes(tokens_input), tensor_alignment) +
-    ggml_allocr_alloc(alloc, tokens_input);
+                            GGML_PAD(ggml_nbytes(target_probs), tensor_alignment) +
-    ggml_allocr_alloc(alloc, target_probs);
+                            tensor_alignment;
-    size_t max_input_size = ggml_allocr_max_size(alloc) + tensor_alignment;
-    ggml_allocr_free(alloc);
     printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f));
 
     // allocate input tensors

examples/infill/CMakeLists.txt

@@ -4,5 +4,5 @@ install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 if(TARGET BUILD_INFO)
-  add_dependencies(${TARGET} BUILD_INFO)
+    add_dependencies(${TARGET} BUILD_INFO)
 endif()

examples/infill/infill.cpp

@@ -39,8 +39,8 @@ static gpt_params               * g_params;
 static std::vector<llama_token> * g_input_tokens;
 static std::ostringstream       * g_output_ss;
 static std::vector<llama_token> * g_output_tokens;
-static bool is_interacting = false;
 
+static bool is_interacting = false;
 
 static void write_logfile(
     const llama_context * ctx, const gpt_params & params, const llama_model * model,
@@ -104,6 +104,7 @@ static void sigint_handler(int signo) {
 
 int main(int argc, char ** argv) {
     gpt_params params;
+    llama_sampling_params & sparams = params.sparams;
     g_params = &params;
 
     if (!gpt_params_parse(argc, argv, params)) {
@@ -206,7 +207,7 @@ int main(int argc, char ** argv) {
     // load the model and apply lora adapter, if any
     LOG("%s: load the model and apply lora adapter, if any\n", __func__);
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
-    if (params.cfg_scale > 1.f) {
+    if (sparams.cfg_scale > 1.f) {
         struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
         ctx_guidance = llama_new_context_with_model(model, lparams);
     }
@@ -233,23 +234,35 @@ int main(int argc, char ** argv) {
     const bool add_bos = llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM;
     LOG("add_bos: %d\n", add_bos);
 
+    bool suff_rm_leading_spc = params.escape;
+    if (suff_rm_leading_spc && params.input_suffix.find_first_of(" ") == 0 && params.input_suffix.size() > 1) {
+        params.input_suffix.erase(0, 1);
+        suff_rm_leading_spc = false;
+    }
     std::vector<llama_token> embd_inp;
-    std::vector<llama_token> inp_pfx = ::llama_tokenize(ctx, params.input_prefix, add_bos);
+    std::vector<llama_token> inp_pfx = ::llama_tokenize(ctx, params.input_prefix, false);
-    std::vector<llama_token> inp_sfx = ::llama_tokenize(ctx, params.input_suffix, add_bos);
+    std::vector<llama_token> inp_sfx = ::llama_tokenize(ctx, params.input_suffix, false);
-    inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(ctx));
+    const int space_token = 29871;
-    inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(ctx));
+    if (suff_rm_leading_spc && inp_sfx[0] == space_token) {
+        inp_sfx.erase(inp_sfx.begin());
+    }
+    inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model));
+    if (add_bos) {
+        inp_pfx.insert(inp_pfx.begin(), llama_token_bos(model));
+    }
+    inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));
     embd_inp = inp_pfx;
     embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
-    embd_inp.push_back(llama_token_middle(ctx));
+    embd_inp.push_back(llama_token_middle(model));
 
     LOG("prefix: \"%s\"\n", log_tostr(params.input_prefix));
     LOG("suffix: \"%s\"\n", log_tostr(params.input_suffix));
-    LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp));
+    LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
 
     // Should not run without any tokens
     if (embd_inp.empty()) {
-        embd_inp.push_back(llama_token_bos(ctx));
+        embd_inp.push_back(llama_token_bos(model));
-        LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp));
+        LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
     }
 
     // Tokenize negative prompt
@@ -257,13 +270,13 @@ int main(int argc, char ** argv) {
     int guidance_offset = 0;
     int original_prompt_len = 0;
     if (ctx_guidance) {
-        LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(params.cfg_negative_prompt));
+        LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));
 
-        guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, add_bos);
+        guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos);
-        LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp));
+        LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());
 
         std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
-        LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp));
+        LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());
 
         original_prompt_len = original_inp.size();
         guidance_offset = (int)guidance_inp.size() - original_prompt_len;
@@ -281,8 +294,8 @@ int main(int argc, char ** argv) {
         params.n_keep = (int)embd_inp.size();
     }
 
-    LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx));
+    LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str());
-    LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx));
+    LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str());
 
 
     // enable interactive mode if interactive start is specified
@@ -300,7 +313,7 @@ int main(int argc, char ** argv) {
 
         if (ctx_guidance) {
             LOG_TEE("\n");
-            LOG_TEE("%s: negative prompt: '%s'\n", __func__, params.cfg_negative_prompt.c_str());
+            LOG_TEE("%s: negative prompt: '%s'\n", __func__, sparams.cfg_negative_prompt.c_str());
             LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
             for (int i = 0; i < (int) guidance_inp.size(); i++) {
                 LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());
@@ -345,39 +358,10 @@ int main(int argc, char ** argv) {
             LOG_TEE("Input suffix: '%s'\n", params.input_suffix.c_str());
         }
     }
-    LOG_TEE("sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty = %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, mirostat_ent = %f\n",
+    LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str());
-            params.repeat_last_n, params.repeat_penalty, params.presence_penalty, params.frequency_penalty, params.top_k, params.tfs_z, params.top_p, params.typical_p, params.temp, params.mirostat, params.mirostat_eta, params.mirostat_tau);
     LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
     LOG_TEE("\n\n");
 
-    struct llama_grammar * grammar = NULL;
-    grammar_parser::parse_state parsed_grammar;
-
-    if (!params.grammar.empty()) {
-        parsed_grammar = grammar_parser::parse(params.grammar.c_str());
-        // will be empty (default) if there are parse errors
-        if (parsed_grammar.rules.empty()) {
-            return 1;
-        }
-        LOG_TEE("%s: grammar:\n", __func__);
-        grammar_parser::print_grammar(stderr, parsed_grammar);
-        LOG_TEE("\n");
-
-        {
-            auto it = params.logit_bias.find(llama_token_eos(ctx));
-            if (it != params.logit_bias.end() && it->second == -INFINITY) {
-                LOG_TEE("%s: warning: EOS token is disabled, which will cause most grammars to fail\n", __func__);
-            }
-        }
-
-        std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
-        grammar = llama_grammar_init(
-            grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
-    }
-
-    // TODO: replace with ring-buffer
-    std::vector<llama_token> last_tokens(n_ctx);
-    std::fill(last_tokens.begin(), last_tokens.end(), 0);
     LOG_TEE("\n#####  Infill mode  #####\n\n");
     if (params.infill) {
         printf("\n************\n");
@@ -420,10 +404,7 @@ int main(int argc, char ** argv) {
     std::vector<llama_token> embd;
     std::vector<llama_token> embd_guidance;
 
-    const int n_vocab = llama_n_vocab(model);
+    struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);
-
-    std::vector<llama_token_data> candidates;
-    candidates.reserve(n_vocab);
 
     while (n_remain != 0 || params.interactive) {
         // predict
@@ -470,7 +451,7 @@ int main(int argc, char ** argv) {
 
                 LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance);
 
-                LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd));
+                LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());
 
             }
 
@@ -498,7 +479,7 @@ int main(int argc, char ** argv) {
                     input_buf  = embd_guidance.data();
                     input_size = embd_guidance.size();
 
-                    LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance));
+                    LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance).c_str());
                 } else {
                     input_buf  = embd.data();
                     input_size = embd.size();
@@ -521,7 +502,7 @@ int main(int argc, char ** argv) {
                     n_eval = params.n_batch;
                 }
 
-                LOG("eval: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd));
+                LOG("eval: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());
 
                 if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) {
                     LOG_TEE("%s : failed to eval\n", __func__);
@@ -540,12 +521,11 @@ int main(int argc, char ** argv) {
 
         if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
 
-            const llama_token id = llama_sample_token(ctx, ctx_guidance, grammar, params, last_tokens, candidates);
+            const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance);
 
-            last_tokens.erase(last_tokens.begin());
+            llama_sampling_accept(ctx_sampling, ctx, id, true);
-            last_tokens.push_back(id);
 
-            LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_tokens));
+            LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, ctx_sampling->prev).c_str());
 
             embd.push_back(id);
 
@@ -561,8 +541,11 @@ int main(int argc, char ** argv) {
             LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed);
             while ((int) embd_inp.size() > n_consumed) {
                 embd.push_back(embd_inp[n_consumed]);
-                last_tokens.erase(last_tokens.begin());
+
-                last_tokens.push_back(embd_inp[n_consumed]);
+                // push the prompt in the sampling context in order to apply repetition penalties later
+                // for the prompt, we don't apply grammar rules
+                llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], false);
+
                 ++n_consumed;
                 if ((int) embd.size() >= params.n_batch) {
                     break;
@@ -594,10 +577,10 @@ int main(int argc, char ** argv) {
         if ((int) embd_inp.size() <= n_consumed) {
 
             // deal with eot token in infill mode
-            if ((last_tokens.back() == llama_token_eot(ctx) || is_interacting) && params.interactive){
+            if ((llama_sampling_last(ctx_sampling) == llama_token_eot(model) || is_interacting) && params.interactive){
                 if(is_interacting && !params.interactive_first) {
                     // print an eot token
-                    printf("%s", llama_token_to_piece(ctx, llama_token_eot(ctx)).c_str());
+                    printf("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str());
                 }
                 fflush(stdout);
                 printf("\n");
@@ -611,7 +594,7 @@ int main(int argc, char ** argv) {
                     buffer += line;
                 } while (another_line);
                 // check if we got an empty line, if so we use the old input
-                if(!buffer.empty() && !(buffer.length() == 1 && buffer[0] == '\n')) {
+                if (!buffer.empty() && !(buffer.length() == 1 && buffer[0] == '\n')) {
                     params.input_prefix = buffer;
                 }
                 buffer.clear();
@@ -621,20 +604,37 @@ int main(int argc, char ** argv) {
                     buffer += line;
                 } while (another_line);
                 // check if we got an empty line
-                if(!buffer.empty() && !(buffer.length() == 1 && buffer[0] == '\n')) {
+                if (!buffer.empty() && !(buffer.length() == 1 && buffer[0] == '\n')) {
                     params.input_suffix = buffer;
                 }
                 buffer.clear();
                 // done taking input, reset color
                 console::set_display(console::reset);
+
+                if (params.escape) {
+                    //process escape sequences, for the initial prompt this is done in common.cpp when we load the params, but for the interactive mode we need to do it here
+                    process_escapes(params.input_prefix);
+                    process_escapes(params.input_suffix);
+                }
+                suff_rm_leading_spc = params.escape;
+                if (suff_rm_leading_spc && params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) {
+                    params.input_suffix.erase(0, 1);
+                    suff_rm_leading_spc = false;
+                }
                 // tokenize new prefix and suffix
-                std::vector<llama_token> inp_pfx = ::llama_tokenize(ctx, params.input_prefix, add_bos);
+                std::vector<llama_token> inp_pfx = ::llama_tokenize(ctx, params.input_prefix, false);
-                std::vector<llama_token> inp_sfx = ::llama_tokenize(ctx, params.input_suffix, add_bos);
+                std::vector<llama_token> inp_sfx = ::llama_tokenize(ctx, params.input_suffix, false);
-                inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(ctx));
+                if (suff_rm_leading_spc && inp_sfx[0] == space_token) {
-                inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(ctx));
+                    inp_sfx.erase(inp_sfx.begin());
+                }
+                inp_pfx.insert(inp_pfx.begin(), llama_token_prefix(model));
+                if (add_bos) {
+                    inp_pfx.insert(inp_pfx.begin(), llama_token_bos(model));
+                }
+                inp_sfx.insert(inp_sfx.begin(), llama_token_suffix(model));
                 embd_inp = inp_pfx;
                 embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
-                embd_inp.push_back(llama_token_middle(ctx));
+                embd_inp.push_back(llama_token_middle(model));
                 embd.clear();
                 embd_guidance.clear();
                 n_remain = params.n_predict;
@@ -644,7 +644,7 @@ int main(int argc, char ** argv) {
                 is_interacting = false;
             }
             // deal with end of text token in interactive mode
-            else if (last_tokens.back() == llama_token_eos(ctx)) {
+            else if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
                 LOG("found EOS token\n");
 
                 if (params.interactive) {
@@ -661,7 +661,7 @@ int main(int argc, char ** argv) {
 
                 if (params.input_prefix_bos) {
                     LOG("adding input prefix BOS token\n");
-                    embd_inp.push_back(llama_token_bos(ctx));
+                    embd_inp.push_back(llama_token_bos(model));
                 }
 
                 std::string buffer;
@@ -696,7 +696,7 @@ int main(int argc, char ** argv) {
                     const size_t original_size = embd_inp.size();
 
                     const auto line_inp = ::llama_tokenize(ctx, buffer, false);
-                    LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp));
+                    LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());
 
                     embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
 
@@ -717,22 +717,14 @@ int main(int argc, char ** argv) {
 
             if (n_past > 0) {
                 if (is_interacting) {
-                    // reset grammar state if we're restarting generation
+                    llama_sampling_reset(ctx_sampling);
-                    if (grammar != NULL) {
-                        llama_grammar_free(grammar);
-
-                        std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
-                        grammar = llama_grammar_init(
-                            grammar_rules.data(), grammar_rules.size(),
-                            parsed_grammar.symbol_ids.at("root"));
-                    }
                 }
                 is_interacting = false;
             }
         }
 
         // end of text token
-        if (!embd.empty() && embd.back() == llama_token_eos(ctx) && !params.interactive) {
+        if (!embd.empty() && embd.back() == llama_token_eos(model) && !params.interactive) {
             break;
         }
 
@@ -744,7 +736,7 @@ int main(int argc, char ** argv) {
         }
     }
     if (!params.interactive && n_remain <= 0) {
-        printf("%s", llama_token_to_piece(ctx, llama_token_eot(ctx)).c_str());
+        printf("%s", llama_token_to_piece(ctx, llama_token_eot(model)).c_str());
         fflush(stdout);
     }
 
@@ -755,9 +747,7 @@ int main(int argc, char ** argv) {
     llama_free(ctx);
     llama_free_model(model);
 
-    if (grammar != NULL) {
+    llama_sampling_free(ctx_sampling);
-        llama_grammar_free(grammar);
-    }
     llama_backend_free();
 
 #ifndef LOG_DISABLE_LOGS

examples/llama-bench/llama-bench.cpp

@@ -933,7 +933,7 @@ struct sql_printer : public printer {
 };
 
 static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) {
-    std::vector<llama_token> tokens(n_batch, llama_token_bos(ctx));
+    std::vector<llama_token> tokens(n_batch, llama_token_bos(llama_get_model(ctx)));
     int n_processed = 0;
 
     llama_set_n_threads(ctx, n_threads, n_threads);
@@ -946,7 +946,7 @@ static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_bat
 }
 
 static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) {
-    llama_token token = llama_token_bos(ctx);
+    llama_token token = llama_token_bos(llama_get_model(ctx));
 
     llama_set_n_threads(ctx, n_threads, n_threads);
 

examples/llava/CMakeLists.txt

@@ -0,0 +1,20 @@
+set(TARGET clip)
+add_library(${TARGET} clip.cpp clip.h)
+install(TARGETS ${TARGET} LIBRARY)
+target_link_libraries(${TARGET} PRIVATE common ggml ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
+if (NOT MSVC)
+    target_compile_options(${TARGET} PRIVATE -Wno-cast-qual) # stb_image.h
+    endif()
+if(TARGET BUILD_INFO)
+    add_dependencies(${TARGET} BUILD_INFO)
+endif()
+
+set(TARGET llava)
+add_executable(${TARGET} llava.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama clip ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
+if(TARGET BUILD_INFO)
+    add_dependencies(${TARGET} BUILD_INFO)
+endif()

examples/llava/README.md

@@ -0,0 +1,57 @@
+# LLaVA
+
+Currently this implementation supports [llava-v1.5](https://huggingface.co/liuhaotian/llava-v1.5-7b) variants.
+
+The pre-converted [7b](https://huggingface.co/mys/ggml_llava-v1.5-7b)
+and [13b](https://huggingface.co/mys/ggml_llava-v1.5-13b)
+models are available.
+
+After API is confirmed, more models will be supported / uploaded.
+
+## Usage
+Build with cmake or run `make llava` to build it.
+
+After building, run: `./llava` to see the usage. For example:
+
+```sh
+./llava -m llava-v1.5-7b/ggml-model-q5_k.gguf --mmproj llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg
+```
+
+**note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so.
+
+## Model conversion
+
+- Clone `llava-v15-7b`` and `clip-vit-large-patch14-336`` locally:
+
+```sh
+git clone https://huggingface.co/liuhaotian/llava-v1.5-7b
+
+git clone https://huggingface.co/openai/clip-vit-large-patch14-336
+```
+
+2. Use `llava-surgery.py` to split the LLaVA model to LLaMA and multimodel projector constituents:
+
+```sh
+python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b
+```
+
+3. Use `convert-image-encoder-to-gguf.py` to convert the LLaVA image encoder to GGUF:
+
+```sh
+python ./examples/llava/convert-image-encoder-to-gguf -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b
+```
+
+4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF:
+
+```sh
+python ./convert.py ../llava-v1.5-7b
+```
+
+Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` directory.
+
+## TODO
+
+- [ ] Support server mode.
+- [ ] Support non-CPU backend for the image encoding part.
+- [ ] Support different sampling methods.
+- [ ] Support more model variants.

examples/llava/clip.h

@@ -0,0 +1,73 @@
+#ifndef CLIP_H
+#define CLIP_H
+
+#include "ggml.h"
+
+struct clip_ctx;
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+struct clip_vision_hparams {
+    int32_t image_size;
+    int32_t patch_size;
+    int32_t hidden_size;
+    int32_t n_intermediate;
+    int32_t projection_dim;
+    int32_t n_head;
+    int32_t n_layer;
+    float eps;
+};
+
+struct clip_ctx * clip_model_load(const char * fname, const int verbosity);
+
+void clip_free(struct clip_ctx * ctx);
+
+size_t clip_embd_nbytes(struct clip_ctx * ctx);
+int clip_n_patches(struct clip_ctx * ctx);
+int clip_n_mmproj_embd(struct clip_ctx * ctx);
+
+// RGB uint8 image
+struct clip_image_u8 {
+    int nx;
+    int ny;
+    uint8_t * data;
+    size_t size;
+};
+
+// RGB float32 image (NHWC)
+// Memory layout: RGBRGBRGB...
+struct clip_image_f32 {
+    int nx;
+    int ny;
+    float * data;
+    size_t size;
+};
+
+struct clip_image_u8_batch {
+    struct clip_image_u8 * data;
+    size_t size;
+};
+
+struct clip_image_f32_batch {
+    struct clip_image_f32 * data;
+    size_t size;
+};
+
+struct clip_image_u8 * make_clip_image_u8();
+struct clip_image_f32 * make_clip_image_f32();
+bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);
+bool clip_image_preprocess(const struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32 * res, const bool pad2square);
+bool clip_image_encode(const struct clip_ctx * ctx, const int n_threads, struct clip_image_f32 * img, float * vec);
+
+bool clip_image_batch_encode(const struct clip_ctx * ctx, const int n_threads, const struct clip_image_f32_batch * imgs,
+                             float * vec);
+
+bool clip_model_quantize(const char * fname_inp, const char * fname_out, const int itype);
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif // CLIP_H

examples/llava/convert-image-encoder-to-gguf.py

@@ -0,0 +1,250 @@
+import argparse
+import os
+import json
+
+import torch
+import numpy as np
+from gguf import *
+from transformers import CLIPModel, CLIPProcessor
+
+TEXT = "clip.text"
+VISION = "clip.vision"
+
+
+def k(raw_key: str, arch: str) -> str:
+    return raw_key.format(arch=arch)
+
+
+def should_skip_tensor(name: str, has_text: bool, has_vision: bool, has_llava: bool) -> bool:
+    if name in (
+        "logit_scale",
+        "text_model.embeddings.position_ids",
+        "vision_model.embeddings.position_ids",
+    ):
+        return True
+
+    if has_llava and name in ["visual_projection.weight", "vision_model.post_layernorm.weight", "vision_model.post_layernorm.bias"]:
+        return True
+
+    if name.startswith("v") and not has_vision:
+        return True
+
+    if name.startswith("t") and not has_text:
+        return True
+
+    return False
+
+
+def get_tensor_name(name: str) -> str:
+    if "projection" in name:
+        return name
+
+    if "mm_projector" in name:
+        return name.replace("model.mm_projector", "mm")
+
+    return name.replace("text_model", "t").replace("vision_model", "v").replace("encoder.layers", "blk").replace("embeddings.", "").replace("_proj", "").replace("self_attn.", "attn_").replace("layer_norm", "ln").replace("layernorm", "ln").replace("mlp.fc1", "ffn_down").replace("mlp.fc2", "ffn_up").replace("embedding", "embd").replace("final", "post").replace("layrnorm", "ln")
+
+
+def bytes_to_unicode():
+    """
+    Returns list of utf-8 byte and a corresponding list of unicode strings.
+    The reversible bpe codes work on unicode strings.
+    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
+    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
+    This is a signficant percentage of your normal, say, 32K bpe vocab.
+    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+    And avoids mapping to whitespace/control characters the bpe code barfs on.
+    """
+    bs = (
+        list(range(ord("!"), ord("~") + 1))
+        + list(range(ord("¡"), ord("¬") + 1))
+        + list(range(ord("®"), ord("ÿ") + 1))
+    )
+    cs = bs[:]
+    n = 0
+    for b in range(2**8):
+        if b not in bs:
+            bs.append(b)
+            cs.append(2**8 + n)
+            n += 1
+    cs = [chr(n) for n in cs]
+    return dict(zip(bs, cs))
+
+
+ap = argparse.ArgumentParser(prog="convert_hf_to_gguf.py")
+ap.add_argument("-m", "--model-dir", help="Path to model directory cloned from HF Hub", required=True)
+ap.add_argument("--use-f32", action="store_true", default=False, help="Use f32 instead of f16")
+ap.add_argument("--text-only", action="store_true", required=False,
+                help="Save a text-only model. It can't be used to encode images")
+ap.add_argument("--vision-only", action="store_true", required=False,
+                help="Save a vision-only model. It can't be used to encode texts")
+ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
+ap.add_argument("--image-mean", nargs=3, type=float, required=False, help="Override image mean values")
+ap.add_argument("--image-std", nargs=3, type=float, required=False, help="Override image std values")
+ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
+
+args = ap.parse_args()
+
+
+if args.text_only and args.vision_only:
+    print("--text-only and --image-only arguments cannot be specified at the same time.")
+    exit(1)
+
+if args.use_f32:
+    print("WARNING: Weights for the convolution op is always saved in f16, as the convolution op in GGML does not support 32-bit kernel weights yet.")
+
+# output in the same directory as the model if output_dir is None
+dir_model = args.model_dir
+
+
+with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
+    vocab = json.load(f)
+    tokens = [key for key in vocab]
+
+with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
+    config = json.load(f)
+    v_hparams = config["vision_config"]
+    t_hparams = config["text_config"]
+
+# possible data types
+#   ftype == 0 -> float32
+#   ftype == 1 -> float16
+#
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+
+ftype = 1
+if args.use_f32:
+    ftype = 0
+
+
+model = CLIPModel.from_pretrained(dir_model)
+processor = CLIPProcessor.from_pretrained(dir_model)
+
+fname_middle = None
+has_text_encoder = True
+has_vision_encoder = True
+has_llava_projector = False
+if args.text_only:
+    fname_middle = "text-"
+    has_vision_encoder = False
+elif args.vision_only:
+    fname_middle = "vision-"
+    has_text_encoder = False
+elif args.llava_projector is not None:
+    fname_middle = "mmproj-"
+    has_text_encoder = False
+    has_llava_projector = True
+else:
+    fname_middle = ""
+
+output_dir = args.output_dir if args.output_dir is not None else dir_model
+os.makedirs(output_dir, exist_ok=True)
+output_prefix = os.path.basename(output_dir).replace("ggml_", "")
+fname_out = os.path.join(output_dir, f"{fname_middle}model-{ftype_str[ftype]}.gguf")
+fout = GGUFWriter(path=fname_out, arch="clip")
+
+fout.add_bool("clip.has_text_encoder", has_text_encoder)
+fout.add_bool("clip.has_vision_encoder", has_vision_encoder)
+fout.add_bool("clip.has_llava_projector", has_llava_projector)
+fout.add_file_type(ftype)
+model_name = config["_name_or_path"] if "_name_or_path" in config else os.path.basename(dir_model)
+fout.add_name(model_name)
+if args.text_only:
+    fout.add_description("text-only CLIP model")
+elif args.vision_only and not has_llava_projector:
+    fout.add_description("vision-only CLIP model")
+elif has_llava_projector:
+    fout.add_description("image encoder for LLaVA")
+else:
+    fout.add_description("two-tower CLIP model")
+
+if has_text_encoder:
+    # text_model hparams
+    fout.add_uint32(k(KEY_CONTEXT_LENGTH, TEXT), t_hparams["max_position_embeddings"])
+    fout.add_uint32(k(KEY_EMBEDDING_LENGTH, TEXT), t_hparams["hidden_size"])
+    fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, TEXT), t_hparams["intermediate_size"])
+    fout.add_uint32("clip.text.projection_dim", t_hparams.get("projection_dim", config["projection_dim"]))
+    fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, TEXT), t_hparams["num_attention_heads"])
+    fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, TEXT), t_hparams["layer_norm_eps"])
+    fout.add_uint32(k(KEY_BLOCK_COUNT, TEXT), t_hparams["num_hidden_layers"])
+    fout.add_token_list(tokens)
+
+if has_vision_encoder:
+    # vision_model hparams
+    fout.add_uint32("clip.vision.image_size", v_hparams["image_size"])
+    fout.add_uint32("clip.vision.patch_size", v_hparams["patch_size"])
+    fout.add_uint32(k(KEY_EMBEDDING_LENGTH, VISION), v_hparams["hidden_size"])
+    fout.add_uint32(k(KEY_FEED_FORWARD_LENGTH, VISION), v_hparams["intermediate_size"])
+    fout.add_uint32("clip.vision.projection_dim", v_hparams.get("projection_dim", config["projection_dim"]))
+    fout.add_uint32(k(KEY_ATTENTION_HEAD_COUNT, VISION), v_hparams["num_attention_heads"])
+    fout.add_float32(k(KEY_ATTENTION_LAYERNORM_EPS, VISION), v_hparams["layer_norm_eps"])
+    block_count = v_hparams["num_hidden_layers"] - 1 if has_llava_projector else v_hparams["num_hidden_layers"]
+    fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), block_count)
+
+    image_mean = processor.image_processor.image_mean if args.image_mean is None else args.image_mean
+    image_std = processor.image_processor.image_std if args.image_std is None else args.image_std
+    fout.add_array("clip.vision.image_mean", image_mean)
+    fout.add_array("clip.vision.image_std", image_std)
+
+use_gelu = v_hparams["hidden_act"] == "gelu"
+fout.add_bool("clip.use_gelu", use_gelu)
+
+
+if has_llava_projector:
+    model.vision_model.encoder.layers.pop(-1)
+    projector = torch.load(args.llava_projector)
+    for name, data in projector.items():
+        name = get_tensor_name(name)
+        if data.ndim == 2:
+            data = data.squeeze().numpy().astype(np.float16)
+        else:
+            data = data.squeeze().numpy().astype(np.float32)
+
+        fout.add_tensor(name, data)
+
+    print("Projector tensors added\n")
+
+state_dict = model.state_dict()
+for name, data in state_dict.items():
+    if should_skip_tensor(name, has_text_encoder, has_vision_encoder, has_llava_projector):
+        # we don't need this
+        print(f"skipping parameter: {name}")
+        continue
+
+    name = get_tensor_name(name)
+    data = data.squeeze().numpy()
+
+    n_dims = len(data.shape)
+
+    # ftype == 0 -> float32, ftype == 1 -> float16
+    ftype_cur = 0
+    if n_dims == 4:
+        print(f"tensor {name} is always saved in f16")
+        data = data.astype(np.float16)
+        ftype_cur = 1
+    elif ftype == 1:
+        if name[-7:] == ".weight" and n_dims == 2:
+            print("  Converting to float16")
+            data = data.astype(np.float16)
+            ftype_cur = 1
+        else:
+            print("  Converting to float32")
+            data = data.astype(np.float32)
+            ftype_cur = 0
+    else:
+        if data.dtype != np.float32:
+            print("  Converting to float32")
+            data = data.astype(np.float32)
+            ftype_cur = 0
+
+    print(f"{name} - {ftype_str[ftype_cur]} - shape = {data.shape}")
+    fout.add_tensor(name, data)
+
+
+fout.write_header_to_file()
+fout.write_kv_data_to_file()
+fout.write_tensors_to_file()
+fout.close()
+
+print("Done. Output file: " + fname_out)

examples/llava/llava-surgery.py

@@ -0,0 +1,46 @@
+import argparse
+import glob
+import os
+import torch
+
+
+ap = argparse.ArgumentParser()
+ap.add_argument("-m", "--model", help="Path to LLaVA v1.5 model")
+args = ap.parse_args()
+
+# find the model part that includes the the multimodal projector weights
+path = sorted(glob.glob(f"{args.model}/pytorch_model*.bin"))[-1]
+checkpoint = torch.load(path)
+
+# get a list of mm tensor names
+mm_tensors = [k for k, v in checkpoint.items() if k.startswith("model.mm_projector")]
+
+# store these tensors in a new dictionary and torch.save them
+projector = {name: checkpoint[name].float() for name in mm_tensors}
+torch.save(projector, f"{args.model}/llava.projector")
+
+# remove these tensors from the checkpoint and save it again
+for name in mm_tensors:
+    del checkpoint[name]
+
+# BakLLaVA models contain CLIP tensors in it
+clip_tensors = [k for k, v in checkpoint.items() if k.startswith("model.vision_tower")]
+if len(clip_tensors) > 0:
+    clip = {name.replace("vision_tower.vision_tower.", ""): checkpoint[name].float() for name in clip_tensors}
+    torch.save(clip, f"{args.model}/llava.clip")
+
+    # remove these tensors
+    for name in clip_tensors:
+        del checkpoint[name]
+
+    # added tokens should be removed to be able to convert Mistral models
+    if os.path.exists(f"{args.model}/added_tokens.json"):
+        with open(f"{args.model}/added_tokens.json", "w") as f:
+            f.write("{}\n")
+
+
+torch.save(checkpoint, path)
+
+print("Done!")
+print(f"Now you can convert {args.model} to a a regular LLaMA GGUF file.")
+print(f"Also, use {args.model}/llava.projector to prepare a llava-encoder.gguf file.")

examples/llava/llava-utils.h

@@ -0,0 +1,147 @@
+#pragma once
+
+// this one and clip lib will be eventually merged to a single lib, let's keep it this way for now
+
+#include "common.h"
+#include "llama.h"
+
+#include <cstdio>
+#include <cstdlib>
+#include <vector>
+
+inline bool eval_image_embd(llama_context * ctx_llama, float * embd, int N, int n_batch, int * n_past) {
+    int n_embd  = llama_n_embd(llama_get_model(ctx_llama));
+
+    for (int i = 0; i < N; i += n_batch) {
+        int n_eval = N - i;
+        if (n_eval > n_batch) {
+            n_eval = n_batch;
+        }
+        llama_batch batch = {int32_t(n_eval), nullptr, (embd+i*n_embd), nullptr, nullptr, nullptr, nullptr, *n_past, 1, 0, };
+        if (llama_decode(ctx_llama, batch)) {
+            fprintf(stderr, "%s : failed to eval\n", __func__);
+            return false;
+        }
+        *n_past += n_eval;
+    }
+    return true;
+}
+
+inline bool eval_tokens(struct llama_context * ctx_llama, std::vector<llama_token> tokens, int n_batch, int * n_past) {
+    int N = (int) tokens.size();
+    for (int i = 0; i < N; i += n_batch) {
+        int n_eval = (int) tokens.size() - i;
+        if (n_eval > n_batch) {
+            n_eval = n_batch;
+        }
+        if (llama_decode(ctx_llama, llama_batch_get_one(&tokens[i], n_eval, *n_past, 0))) {
+            fprintf(stderr, "%s : failed to eval\n", __func__);
+            return false;
+        }
+        *n_past += n_eval;
+    }
+    return true;
+}
+
+inline bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) {
+    std::vector<llama_token> tokens;
+    tokens.push_back(id);
+    return eval_tokens(ctx_llama, tokens, 1, n_past);
+}
+
+inline bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){
+    std::string              str2     = str;
+    std::vector<llama_token> embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos);
+    eval_tokens(ctx_llama, embd_inp, n_batch, n_past);
+    return true;
+}
+
+// TODO: use common/sampling.h
+inline llama_token sample_id(llama_context * ctx_llama, gpt_params & params) {
+    auto & sparams = params.sparams;
+
+    // out of user input, sample next token
+    const float   temp      = sparams.temp;
+    const int32_t top_k     = sparams.top_k <= 0 ? llama_n_vocab(llama_get_model(ctx_llama)) : sparams.top_k;
+    const float   top_p     = sparams.top_p;
+    const float   tfs_z     = sparams.tfs_z;
+    const float   typical_p = sparams.typical_p;
+    // const int32_t repeat_last_n   = sparams.repeat_last_n < 0 ? n_ctx : sparams.repeat_last_n;
+    // const float   repeat_penalty  = sparams.repeat_penalty;
+    // const float   alpha_presence  = sparams.presence_penalty;
+    // const float   alpha_frequency = sparams.frequency_penalty;
+    const int     mirostat     = sparams.mirostat;
+    const float   mirostat_tau = sparams.mirostat_tau;
+    const float   mirostat_eta = sparams.mirostat_eta;
+    // const bool    penalize_nl     = sparams.penalize_nl;
+
+    llama_token id = 0;
+    {
+        auto logits  = llama_get_logits(ctx_llama);
+        auto n_vocab = llama_n_vocab(llama_get_model(ctx_llama));
+
+        // Apply params.logit_bias map
+        for (auto it = sparams.logit_bias.begin(); it != sparams.logit_bias.end(); it++) {
+            logits[it->first] += it->second;
+        }
+
+        std::vector<llama_token_data> candidates;
+        candidates.reserve(n_vocab);
+        for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+            candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
+        }
+
+        llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+
+        // TODO: Apply penalties
+        // float nl_logit = logits[llama_token_nl(ctx)];
+        // auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
+        // llama_sample_repetition_penalty(ctx, &candidates_p,
+        //      last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
+        //      last_n_repeat, repeat_penalty);
+        // llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
+        // last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
+        // last_n_repeat, alpha_frequency, alpha_presence);
+        // if (!penalize_nl) {
+        //     logits[llama_token_nl(ctx)] = nl_logit;
+        // }
+
+        if (temp <= 0) {
+              // Greedy sampling
+            id = llama_sample_token_greedy(ctx_llama, &candidates_p);
+        } else {
+            if (mirostat == 1) {
+                static float mirostat_mu = 2.0f * mirostat_tau;
+                const  int mirostat_m    = 100;
+                llama_sample_temp(ctx_llama, &candidates_p, temp);
+                id = llama_sample_token_mirostat(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
+            } else if (mirostat == 2) {
+                static float mirostat_mu = 2.0f * mirostat_tau;
+                llama_sample_temp(ctx_llama, &candidates_p, temp);
+                id = llama_sample_token_mirostat_v2(ctx_llama, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
+            } else {
+                  // Temperature sampling
+                llama_sample_top_k(ctx_llama, &candidates_p, top_k, 1);
+                llama_sample_tail_free(ctx_llama, &candidates_p, tfs_z, 1);
+                llama_sample_typical(ctx_llama, &candidates_p, typical_p, 1);
+                llama_sample_top_p(ctx_llama, &candidates_p, top_p, 1);
+                llama_sample_temp(ctx_llama, &candidates_p, temp);
+                id = llama_sample_token(ctx_llama, &candidates_p);
+            }
+        }
+    }
+
+    return id;
+}
+
+inline const char * sample(struct llama_context * ctx_llama, gpt_params & params, int * n_past) {
+    int id = sample_id(ctx_llama, params);
+    static std::string ret;
+    if (id == llama_token_eos(llama_get_model(ctx_llama))) {
+        ret = "</s>";
+    } else {
+        ret = llama_token_to_piece(ctx_llama, id);
+    }
+    eval_id(ctx_llama, id, n_past);
+    return ret.c_str();
+}

examples/llava/llava.cpp

@@ -0,0 +1,164 @@
+#include "clip.h"
+#include "llava-utils.h"
+#include "common.h"
+#include "llama.h"
+
+#include <cstdio>
+#include <cstdlib>
+#include <vector>
+
+static void show_additional_info(int /*argc*/, char ** argv) {
+    printf("\n example usage: %s -m <llava-v1.5-7b/ggml-model-q5_k.gguf> --mmproj <llava-v1.5-7b/mmproj-model-f16.gguf> --image <path/to/an/image.jpg> [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]);
+    printf("  note: a lower temperature value like 0.1 is recommended for better quality.\n");
+}
+
+int main(int argc, char ** argv) {
+    ggml_time_init();
+
+    gpt_params params;
+
+    if (!gpt_params_parse(argc, argv, params)) {
+        show_additional_info(argc, argv);
+        return 1;
+    }
+
+    if (params.mmproj.empty() || params.image.empty()) {
+        gpt_print_usage(argc, argv, params);
+        show_additional_info(argc, argv);
+        return 1;
+    }
+
+    const char * clip_path = params.mmproj.c_str();
+    const char * img_path = params.image.c_str();
+
+    if (params.prompt.empty()) {
+        params.prompt = "describe the image in detail.";
+    }
+
+    auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1);
+
+    // load and preprocess the image
+    clip_image_u8 img;
+    clip_image_f32 img_res;
+
+    if (!clip_image_load_from_file(img_path, &img)) {
+        fprintf(stderr, "%s: is %s really an image file?\n", __func__, img_path);
+
+        clip_free(ctx_clip);
+        return 1;
+    }
+
+    if (!clip_image_preprocess(ctx_clip, &img, &img_res, /*pad2square =*/ true)) {
+        fprintf(stderr, "%s: unable to preprocess %s\n", __func__, img_path);
+
+        clip_free(ctx_clip);
+        return 1;
+    }
+
+    int n_img_pos  = clip_n_patches(ctx_clip);
+    int n_img_embd = clip_n_mmproj_embd(ctx_clip);
+
+    float * image_embd = (float *)malloc(clip_embd_nbytes(ctx_clip));
+
+    if (!image_embd) {
+        fprintf(stderr, "Unable to allocate memory for image embeddings\n");
+
+        return 1;
+    }
+
+    const int64_t t_img_enc_start_us = ggml_time_us();
+    if (!clip_image_encode(ctx_clip, params.n_threads, &img_res, image_embd)) {
+        fprintf(stderr, "Unable to encode image\n");
+
+        return 1;
+    }
+    const int64_t t_img_enc_end_us = ggml_time_us();
+
+    // we get the embeddings, free up the memory required for CLIP
+    clip_free(ctx_clip);
+
+    llama_backend_init(params.numa);
+
+    llama_model_params model_params              = llama_model_default_params();
+                       model_params.n_gpu_layers = params.n_gpu_layers;
+                       model_params.main_gpu     = params.main_gpu;
+                       model_params.tensor_split = params.tensor_split;
+                       model_params.use_mmap     = params.use_mmap;
+                       model_params.use_mlock    = params.use_mlock;
+
+    llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params);
+    if (model == NULL) {
+        fprintf(stderr , "%s: error: unable to load model\n" , __func__);
+        return 1;
+    }
+
+    llama_context_params ctx_params = llama_context_default_params();
+
+    ctx_params.n_ctx           = params.n_ctx < 2048 ? 2048 : params.n_ctx; // we need a longer context size to process image embeddings
+    ctx_params.n_threads       = params.n_threads;
+    ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
+    ctx_params.seed            = params.seed;
+
+    llama_context * ctx_llama = llama_new_context_with_model(model, ctx_params);
+
+    if (ctx_llama == NULL) {
+        fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__);
+        return 1;
+    }
+
+    // make sure that the correct mmproj was used, i.e., compare apples to apples
+    const int n_llama_embd = llama_n_embd(llama_get_model(ctx_llama));
+
+    if (n_img_embd != n_llama_embd) {
+        printf("%s: embedding dim of the multimodal projector (%d) is not equal to that of LLaMA (%d). Make sure that you use the correct mmproj file.\n", __func__, n_img_embd, n_llama_embd);
+
+        llama_free(ctx_llama);
+        llama_free_model(model);
+        llama_backend_free();
+        free(image_embd);
+
+        return 1;
+    }
+
+    // process the prompt
+    // llava chat format is "<system_prompt>USER: <image_embeddings>\n<textual_prompt>\nASSISTANT:"
+
+    int n_past = 0;
+
+    const int max_tgt_len = params.n_predict < 0 ? 256 : params.n_predict;
+
+    eval_string(ctx_llama, "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:", params.n_batch, &n_past, true);
+    eval_image_embd(ctx_llama, image_embd, n_img_pos, params.n_batch, &n_past);
+    eval_string(ctx_llama, (params.prompt + "\nASSISTANT:").c_str(), params.n_batch, &n_past, false);
+
+    // generate the response
+
+    printf("\n");
+    printf("prompt: '%s'\n", params.prompt.c_str());
+    printf("\n");
+
+    for (int i = 0; i < max_tgt_len; i++) {
+        const char * tmp = sample(ctx_llama, params, &n_past);
+        if (strcmp(tmp, "</s>") == 0) break;
+
+        printf("%s", tmp);
+        fflush(stdout);
+    }
+
+    printf("\n");
+
+    {
+        const float t_img_enc_ms = (t_img_enc_end_us - t_img_enc_start_us) / 1000.0;
+
+        printf("\n%s: image encoded in %8.2f ms by CLIP (%8.2f ms per image patch)\n", __func__, t_img_enc_ms, t_img_enc_ms / n_img_pos);
+    }
+
+    llama_print_timings(ctx_llama);
+
+    llama_free(ctx_llama);
+    llama_free_model(model);
+    llama_backend_free();
+    free(image_embd);
+
+    return 0;
+}

examples/main/main.cpp

@@ -3,7 +3,6 @@
 #include "console.h"
 #include "llama.h"
 #include "build-info.h"
-#include "grammar-parser.h"
 
 #include <cassert>
 #include <cinttypes>
@@ -113,6 +112,7 @@ int main(int argc, char ** argv) {
     if (!gpt_params_parse(argc, argv, params)) {
         return 1;
     }
+    llama_sampling_params & sparams = params.sparams;
 
 #ifndef LOG_DISABLE_LOGS
     log_set_target(log_filename_generator("main", "log"));
@@ -187,7 +187,7 @@ int main(int argc, char ** argv) {
     // load the model and apply lora adapter, if any
     LOG("%s: load the model and apply lora adapter, if any\n", __func__);
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
-    if (params.cfg_scale > 1.f) {
+    if (sparams.cfg_scale > 1.f) {
         struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
         ctx_guidance = llama_new_context_with_model(model, lparams);
     }
@@ -245,19 +245,19 @@ int main(int argc, char ** argv) {
 
     if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) {
         LOG("tokenize the prompt\n");
-        embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
+        embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
     } else {
         LOG("use session tokens\n");
         embd_inp = session_tokens;
     }
 
     LOG("prompt: \"%s\"\n", log_tostr(params.prompt));
-    LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp));
+    LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
 
     // Should not run without any tokens
     if (embd_inp.empty()) {
-        embd_inp.push_back(llama_token_bos(ctx));
+        embd_inp.push_back(llama_token_bos(model));
-        LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp));
+        LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str());
     }
 
     // Tokenize negative prompt
@@ -265,13 +265,13 @@ int main(int argc, char ** argv) {
     int guidance_offset = 0;
     int original_prompt_len = 0;
     if (ctx_guidance) {
-        LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(params.cfg_negative_prompt));
+        LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt));
 
-        guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, add_bos);
+        guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, add_bos, true);
-        LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp));
+        LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str());
 
-        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
+        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
-        LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp));
+        LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str());
 
         original_prompt_len = original_inp.size();
         guidance_offset = (int)guidance_inp.size() - original_prompt_len;
@@ -304,6 +304,9 @@ int main(int argc, char ** argv) {
             LOG_TEE("%s: session file matches %zu / %zu tokens of prompt\n",
                 __func__, n_matching_session_tokens, embd_inp.size());
         }
+
+        // remove any "future" tokens that we might have inherited from the previous session
+        llama_kv_cache_tokens_rm(ctx, n_matching_session_tokens, -1);
     }
 
     LOGLN(
@@ -324,11 +327,11 @@ int main(int argc, char ** argv) {
     }
 
     // prefix & suffix for instruct mode
-    const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos);
+    const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos, true);
-    const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n",    false);
+    const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n",    false,   true);
 
-    LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx));
+    LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str());
-    LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx));
+    LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str());
 
     // in instruct mode, we inject a prefix and a suffix to each input by the user
     if (params.instruct) {
@@ -351,7 +354,7 @@ int main(int argc, char ** argv) {
 
         if (ctx_guidance) {
             LOG_TEE("\n");
-            LOG_TEE("%s: negative prompt: '%s'\n", __func__, params.cfg_negative_prompt.c_str());
+            LOG_TEE("%s: negative prompt: '%s'\n", __func__, sparams.cfg_negative_prompt.c_str());
             LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
             for (int i = 0; i < (int) guidance_inp.size(); i++) {
                 LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());
@@ -387,6 +390,12 @@ int main(int argc, char ** argv) {
         if (!params.antiprompt.empty()) {
             for (const auto & antiprompt : params.antiprompt) {
                 LOG_TEE("Reverse prompt: '%s'\n", antiprompt.c_str());
+                if (params.verbose_prompt) {
+                    auto tmp = ::llama_tokenize(ctx, antiprompt, false, true);
+                    for (int i = 0; i < (int) tmp.size(); i++) {
+                        LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
+                    }
+                }
             }
         }
 
@@ -396,45 +405,27 @@ int main(int argc, char ** argv) {
 
         if (!params.input_prefix.empty()) {
             LOG_TEE("Input prefix: '%s'\n", params.input_prefix.c_str());
+            if (params.verbose_prompt) {
+                auto tmp = ::llama_tokenize(ctx, params.input_prefix, true, true);
+                for (int i = 0; i < (int) tmp.size(); i++) {
+                    LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
+                }
+            }
         }
 
         if (!params.input_suffix.empty()) {
             LOG_TEE("Input suffix: '%s'\n", params.input_suffix.c_str());
-        }
+            if (params.verbose_prompt) {
-    }
+                auto tmp = ::llama_tokenize(ctx, params.input_suffix, false, true);
-    LOG_TEE("sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty = %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, mirostat_ent = %f\n",
+                for (int i = 0; i < (int) tmp.size(); i++) {
-            params.repeat_last_n, params.repeat_penalty, params.presence_penalty, params.frequency_penalty, params.top_k, params.tfs_z, params.top_p, params.typical_p, params.temp, params.mirostat, params.mirostat_eta, params.mirostat_tau);
+                    LOG_TEE("%6d -> '%s'\n", tmp[i], llama_token_to_piece(ctx, tmp[i]).c_str());
-    LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
+                }
-    LOG_TEE("\n\n");
-
-    struct llama_grammar * grammar = NULL;
-    grammar_parser::parse_state parsed_grammar;
-
-    if (!params.grammar.empty()) {
-        parsed_grammar = grammar_parser::parse(params.grammar.c_str());
-        // will be empty (default) if there are parse errors
-        if (parsed_grammar.rules.empty()) {
-            return 1;
-        }
-        LOG_TEE("%s: grammar:\n", __func__);
-        grammar_parser::print_grammar(stderr, parsed_grammar);
-        LOG_TEE("\n");
-
-        {
-            auto it = params.logit_bias.find(llama_token_eos(ctx));
-            if (it != params.logit_bias.end() && it->second == -INFINITY) {
-                LOG_TEE("%s: warning: EOS token is disabled, which will cause most grammars to fail\n", __func__);
             }
         }
-
-        std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
-        grammar = llama_grammar_init(
-            grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
     }
-
+    LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str());
-    // TODO: replace with ring-buffer
+    LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
-    std::vector<llama_token> last_tokens(n_ctx);
+    LOG_TEE("\n\n");
-    std::fill(last_tokens.begin(), last_tokens.end(), 0);
 
     if (params.interactive) {
         const char *control_message;
@@ -475,10 +466,7 @@ int main(int argc, char ** argv) {
     std::vector<llama_token> embd;
     std::vector<llama_token> embd_guidance;
 
-    const int n_vocab = llama_n_vocab(model);
+    struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);
-
-    std::vector<llama_token_data> candidates;
-    candidates.reserve(n_vocab);
 
     while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
         // predict
@@ -525,7 +513,7 @@ int main(int argc, char ** argv) {
 
                 LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance);
 
-                LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd));
+                LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());
 
                 LOG("clear session path\n");
                 path_session.clear();
@@ -551,14 +539,10 @@ int main(int argc, char ** argv) {
                 if (i > 0) {
                     embd.erase(embd.begin(), embd.begin() + i);
                 }
-
-                // remove any "future" tokens that we might have inherited from the session from the KV cache
-                llama_kv_cache_tokens_rm(ctx, n_past, -1);
             }
 
             // evaluate tokens in batches
             // embd is typically prepared beforehand to fit within a batch, but not always
-
             if (ctx_guidance) {
                 int input_size = 0;
                 llama_token * input_buf = NULL;
@@ -580,7 +564,7 @@ int main(int argc, char ** argv) {
                     input_buf  = embd_guidance.data();
                     input_size = embd_guidance.size();
 
-                    LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance));
+                    LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance).c_str());
                 } else {
                     input_buf  = embd.data();
                     input_size = embd.size();
@@ -603,7 +587,7 @@ int main(int argc, char ** argv) {
                     n_eval = params.n_batch;
                 }
 
-                LOG("eval: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd));
+                LOG("eval: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str());
 
                 if (llama_decode(ctx, llama_batch_get_one(&embd[i], n_eval, n_past, 0))) {
                     LOG_TEE("%s : failed to eval\n", __func__);
@@ -633,12 +617,11 @@ int main(int argc, char ** argv) {
                 LOG("saved session to %s\n", path_session.c_str());
             }
 
-            const llama_token id = llama_sample_token(ctx, ctx_guidance, grammar, params, last_tokens, candidates);
+            const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance);
 
-            last_tokens.erase(last_tokens.begin());
+            llama_sampling_accept(ctx_sampling, ctx, id, true);
-            last_tokens.push_back(id);
 
-            LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_tokens));
+            LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, ctx_sampling->prev).c_str());
 
             embd.push_back(id);
 
@@ -654,8 +637,11 @@ int main(int argc, char ** argv) {
             LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed);
             while ((int) embd_inp.size() > n_consumed) {
                 embd.push_back(embd_inp[n_consumed]);
-                last_tokens.erase(last_tokens.begin());
+
-                last_tokens.push_back(embd_inp[n_consumed]);
+                // push the prompt in the sampling context in order to apply repetition penalties later
+                // for the prompt, we don't apply grammar rules
+                llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], false);
+
                 ++n_consumed;
                 if ((int) embd.size() >= params.n_batch) {
                     break;
@@ -685,12 +671,10 @@ int main(int argc, char ** argv) {
 
         // if not currently processing queued inputs;
         if ((int) embd_inp.size() <= n_consumed) {
-            // check for reverse prompt
+            // check for reverse prompt in the last n_prev tokens
             if (!params.antiprompt.empty()) {
-                std::string last_output;
+                const int n_prev = 32;
-                for (auto id : last_tokens) {
+                const std::string last_output = llama_sampling_prev_str(ctx_sampling, ctx, n_prev);
-                    last_output += llama_token_to_piece(ctx, id);
-                }
 
                 is_antiprompt = false;
                 // Check if each of the reverse prompts appears at the end of the output.
@@ -717,13 +701,13 @@ int main(int argc, char ** argv) {
             }
 
             // deal with end of text token in interactive mode
-            if (last_tokens.back() == llama_token_eos(ctx)) {
+            if (llama_sampling_last(ctx_sampling) == llama_token_eos(model)) {
                 LOG("found EOS token\n");
 
                 if (params.interactive) {
                     if (!params.antiprompt.empty()) {
                         // tokenize and inject first reverse prompt
-                        const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false);
+                        const auto first_antiprompt = ::llama_tokenize(ctx, params.antiprompt.front(), false, true);
                         embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end());
                         is_antiprompt = true;
                     }
@@ -744,14 +728,13 @@ int main(int argc, char ** argv) {
 
                 if (params.input_prefix_bos) {
                     LOG("adding input prefix BOS token\n");
-                    embd_inp.push_back(llama_token_bos(ctx));
+                    embd_inp.push_back(llama_token_bos(model));
                 }
 
                 std::string buffer;
                 if (!params.input_prefix.empty()) {
                     LOG("appending input prefix: '%s'\n", params.input_prefix.c_str());
-                    buffer += params.input_prefix;
+                    printf("%s", params.input_prefix.c_str());
-                    printf("%s", buffer.c_str());
                 }
 
                 // color user input only
@@ -773,7 +756,6 @@ int main(int argc, char ** argv) {
                     // append input suffix if any
                     if (!params.input_suffix.empty()) {
                         LOG("appending input suffix: '%s'\n", params.input_suffix.c_str());
-                        buffer += params.input_suffix;
                         printf("%s", params.input_suffix.c_str());
                     }
 
@@ -787,11 +769,18 @@ int main(int argc, char ** argv) {
                         n_consumed = embd_inp.size();
                         embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end());
                     }
+                    if (params.escape) {
+                        process_escapes(buffer);
+                    }
 
-                    const auto line_inp = ::llama_tokenize(ctx, buffer, false);
+                    const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true);
-                    LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp));
+                    const auto line_inp = ::llama_tokenize(ctx, buffer,              false, false);
+                    const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true);
+                    LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());
 
+                    embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end());
                     embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
+                    embd_inp.insert(embd_inp.end(), line_sfx.begin(), line_sfx.end());
 
                     // instruct mode: insert response suffix
                     if (params.instruct) {
@@ -816,22 +805,14 @@ int main(int argc, char ** argv) {
 
             if (n_past > 0) {
                 if (is_interacting) {
-                    // reset grammar state if we're restarting generation
+                    llama_sampling_reset(ctx_sampling);
-                    if (grammar != NULL) {
-                        llama_grammar_free(grammar);
-
-                        std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
-                        grammar = llama_grammar_init(
-                            grammar_rules.data(), grammar_rules.size(),
-                            parsed_grammar.symbol_ids.at("root"));
-                    }
                 }
                 is_interacting = false;
             }
         }
 
         // end of text token
-        if (!embd.empty() && embd.back() == llama_token_eos(ctx) && !(params.instruct || params.interactive)) {
+        if (!embd.empty() && embd.back() == llama_token_eos(model) && !(params.instruct || params.interactive)) {
             LOG_TEE(" [end of text]\n");
             break;
         }
@@ -856,9 +837,7 @@ int main(int argc, char ** argv) {
     llama_free(ctx);
     llama_free_model(model);
 
-    if (grammar != NULL) {
+    llama_sampling_free(ctx_sampling);
-        llama_grammar_free(grammar);
-    }
     llama_backend_free();
 
 #ifndef LOG_DISABLE_LOGS

examples/parallel/parallel.cpp

@@ -51,6 +51,12 @@ static std::vector<std::string> k_prompts = {
 };
 
 struct client {
+    ~client() {
+        if (ctx_sampling) {
+            llama_sampling_free(ctx_sampling);
+        }
+    }
+
     int32_t id = 0;
 
     llama_seq_id seq_id = -1;
@@ -68,7 +74,7 @@ struct client {
     std::string prompt;
     std::string response;
 
-    std::vector<llama_token> tokens_prev;
+    struct llama_sampling_context * ctx_sampling = nullptr;
 };
 
 static void print_date_time() {
@@ -145,20 +151,15 @@ int main(int argc, char ** argv) {
     fprintf(stderr, "\n\n");
     fflush(stderr);
 
-    const int n_ctx   = llama_n_ctx(ctx);
+    const int n_ctx = llama_n_ctx(ctx);
-    const int n_vocab = llama_n_vocab(model);
 
     std::vector<client> clients(n_clients);
     for (size_t i = 0; i < clients.size(); ++i) {
         auto & client = clients[i];
         client.id = i;
-        client.tokens_prev.resize(std::max(256, params.n_predict));
+        client.ctx_sampling = llama_sampling_init(params.sparams);
-        std::fill(client.tokens_prev.begin(), client.tokens_prev.end(), 0);
     }
 
-    std::vector<llama_token_data> candidates;
-    candidates.reserve(n_vocab);
-
     std::vector<llama_token> tokens_system;
     tokens_system = ::llama_tokenize(ctx, k_system, true);
     const int32_t n_tokens_system = tokens_system.size();
@@ -167,7 +168,7 @@ int main(int argc, char ** argv) {
 
     // the max batch size is as large as the context to handle cases where we get very long input prompt from multiple
     // users. regardless of the size, the main loop will chunk the batch into a maximum of params.n_batch tokens at a time
-    llama_batch batch = llama_batch_init(n_ctx, 0);
+    llama_batch batch = llama_batch_init(n_ctx, 0, 1);
 
     int32_t n_total_prompt = 0;
     int32_t n_total_gen    = 0;
@@ -182,13 +183,8 @@ int main(int argc, char ** argv) {
     {
         LOG_TEE("%s: Evaluating the system prompt ...\n", __func__);
 
-        batch.n_tokens = n_tokens_system;
+        for (int32_t i = 0; i < n_tokens_system; ++i) {
-
+            llama_batch_add(batch, tokens_system[i], i, { 0 }, false);
-        for (int32_t i = 0; i < batch.n_tokens; ++i) {
-            batch.token[i]  = tokens_system[i];
-            batch.pos[i]    = i;
-            batch.seq_id[i] = 0;
-            batch.logits[i] = false;
         }
 
         if (llama_decode(ctx, batch) != 0) {
@@ -207,7 +203,7 @@ int main(int argc, char ** argv) {
     LOG_TEE("Processing requests ...\n\n");
 
     while (true) {
-        batch.n_tokens = 0;
+        llama_batch_clear(batch);
 
         // decode any currently ongoing sequences
         for (auto & client : clients) {
@@ -215,15 +211,11 @@ int main(int argc, char ** argv) {
                 continue;
             }
 
-            batch.token [batch.n_tokens] = client.sampled;
-            batch.pos   [batch.n_tokens] = n_tokens_system + client.n_prompt + client.n_decoded;
-            batch.seq_id[batch.n_tokens] = client.id;
-            batch.logits[batch.n_tokens] = true;
-
-            client.n_decoded += 1;
             client.i_batch = batch.n_tokens;
 
-            batch.n_tokens += 1;
+            llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id }, true);
+
+            client.n_decoded += 1;
         }
 
         if (batch.n_tokens == 0) {
@@ -248,18 +240,14 @@ int main(int argc, char ** argv) {
                     client.prompt   = client.input + "\nAssistant:";
                     client.response = "";
 
-                    std::fill(client.tokens_prev.begin(), client.tokens_prev.end(), 0);
+                    llama_sampling_reset(client.ctx_sampling);
 
                     // do not prepend BOS because we have a system prompt!
                     std::vector<llama_token> tokens_prompt;
                     tokens_prompt = ::llama_tokenize(ctx, client.prompt, false);
 
                     for (size_t i = 0; i < tokens_prompt.size(); ++i) {
-                        batch.token [batch.n_tokens] = tokens_prompt[i];
+                        llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id }, false);
-                        batch.pos   [batch.n_tokens] = i + n_tokens_system;
-                        batch.seq_id[batch.n_tokens] = client.id;
-                        batch.logits[batch.n_tokens] = false;
-                        batch.n_tokens += 1;
                     }
 
                     // extract the logits only for the last token
@@ -302,11 +290,12 @@ int main(int argc, char ** argv) {
 
             llama_batch batch_view = {
                 n_tokens,
-                batch.token  + i,
+                batch.token    + i,
                 nullptr,
-                batch.pos    + i,
+                batch.pos      + i,
-                batch.seq_id + i,
+                batch.n_seq_id + i,
-                batch.logits + i,
+                batch.seq_id   + i,
+                batch.logits   + i,
                 0, 0, 0, // unused
             };
 
@@ -339,7 +328,9 @@ int main(int argc, char ** argv) {
                 //printf("client %d, seq %d, token %d, pos %d, batch %d\n",
                 //        client.id, client.seq_id, client.sampled, client.n_decoded, client.i_batch);
 
-                const llama_token id = llama_sample_token(ctx, NULL, NULL, params, client.tokens_prev, candidates, client.i_batch - i);
+                const llama_token id = llama_sampling_sample(client.ctx_sampling, ctx, NULL, client.i_batch - i);
+
+                llama_sampling_accept(client.ctx_sampling, ctx, id, true);
 
                 if (client.n_decoded == 1) {
                     // start measuring generation time after the first token to make sure all concurrent clients
@@ -347,11 +338,8 @@ int main(int argc, char ** argv) {
                     client.t_start_gen = ggml_time_us();
                 }
 
-                // remember which tokens were sampled - used for repetition penalties during sampling
-                client.tokens_prev.erase(client.tokens_prev.begin());
-                client.tokens_prev.push_back(id);
-
                 const std::string token_str = llama_token_to_piece(ctx, id);
+
                 client.response += token_str;
                 client.sampled = id;
 
@@ -359,7 +347,7 @@ int main(int argc, char ** argv) {
                 //        client.id, client.seq_id, id, client.n_decoded, client.i_batch, token_str.c_str());
 
                 if (client.n_decoded > 2 &&
-                        (id == llama_token_eos(ctx) ||
+                        (id == llama_token_eos(model) ||
                          (params.n_predict > 0 && client.n_decoded + client.n_prompt >= params.n_predict) ||
                          client.response.find("User:") != std::string::npos ||
                          client.response.find('\n') != std::string::npos)) {

examples/perplexity/perplexity.cpp

@@ -227,7 +227,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
 
             // add BOS token for the first batch of each chunk
             if (add_bos && j == 0) {
-                tokens[batch_start] = llama_token_bos(ctx);
+                tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
             }
 
             const auto batch_logits = llama_get_logits(ctx);
@@ -350,7 +350,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
 
             // add BOS token for the first batch of each chunk
             if (add_bos && j == 0) {
-                tokens[batch_start] = llama_token_bos(ctx);
+                tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
             }
 
             if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {

examples/save-load-state/save-load-state.cpp

@@ -8,9 +8,7 @@
 
 int main(int argc, char ** argv) {
     gpt_params params;
-    params.seed = 42;
+
-    params.n_threads = 4;
-    params.repeat_last_n = 64;
     params.prompt = "The quick brown fox";
 
     if (!gpt_params_parse(argc, argv, params)) {
@@ -24,56 +22,49 @@ int main(int argc, char ** argv) {
     }
 
     auto n_past = 0;
-    auto last_n_tokens_data = std::vector<llama_token>(params.repeat_last_n, 0);
+
+    std::string result0;
+    std::string result1;
 
     // init
     llama_model * model;
     llama_context * ctx;
 
-    std::tie(model, ctx) = llama_init_from_gpt_params( params );
+    std::tie(model, ctx) = llama_init_from_gpt_params(params);
-    if (model == nullptr) {
+    if (model == nullptr || ctx == nullptr) {
-        return 1;
+        fprintf(stderr, "%s : failed to init\n", __func__);
-    }
-    if (ctx == nullptr) {
-        llama_free_model(model);
         return 1;
     }
+
+    // tokenize prompt
     auto tokens = llama_tokenize(ctx, params.prompt, true);
-    auto n_prompt_tokens = tokens.size();
-    if (n_prompt_tokens < 1) {
-        fprintf(stderr, "%s : failed to tokenize prompt\n", __func__);
-        llama_free(ctx);
-        llama_free_model(model);
-        return 1;
-    }
 
     // evaluate prompt
-    llama_decode(ctx, llama_batch_get_one(tokens.data(), n_prompt_tokens, n_past, 0));
+    llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), n_past, 0));
+    n_past += tokens.size();
 
-    last_n_tokens_data.insert(last_n_tokens_data.end(), tokens.data(), tokens.data() + n_prompt_tokens);
+    // save state (rng, logits, embedding and kv_cache) to file
-    n_past += n_prompt_tokens;
-
-    const size_t state_size = llama_get_state_size(ctx);
-    uint8_t * state_mem = new uint8_t[state_size];
-
-    // Save state (rng, logits, embedding and kv_cache) to file
     {
-        FILE *fp_write = fopen("dump_state.bin", "wb");
+        std::vector<uint8_t> state_mem(llama_get_state_size(ctx));
-        llama_copy_state_data(ctx, state_mem); // could also copy directly to memory mapped file
+
-        fwrite(state_mem, 1, state_size, fp_write);
+        {
-        fclose(fp_write);
+            FILE *fp_write = fopen("dump_state.bin", "wb");
+            llama_copy_state_data(ctx, state_mem.data()); // could also copy directly to memory mapped file
+            fwrite(state_mem.data(), 1, state_mem.size(), fp_write);
+            fclose(fp_write);
+        }
     }
 
     // save state (last tokens)
-    const auto last_n_tokens_data_saved = std::vector<llama_token>(last_n_tokens_data);
     const auto n_past_saved = n_past;
 
     // first run
-    printf("\n%s", params.prompt.c_str());
+    printf("\nfirst run: %s", params.prompt.c_str());
 
     for (auto i = 0; i < params.n_predict; i++) {
         auto * logits = llama_get_logits(ctx);
         auto n_vocab = llama_n_vocab(model);
+
         std::vector<llama_token_data> candidates;
         candidates.reserve(n_vocab);
         for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
@@ -82,9 +73,10 @@ int main(int argc, char ** argv) {
         llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
         auto next_token = llama_sample_token(ctx, &candidates_p);
         auto next_token_str = llama_token_to_piece(ctx, next_token);
-        last_n_tokens_data.push_back(next_token);
 
         printf("%s", next_token_str.c_str());
+        result0 += next_token_str;
+
         if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0))) {
             fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
             llama_free(ctx);
@@ -102,32 +94,28 @@ int main(int argc, char ** argv) {
     // make new context
     auto * ctx2 = llama_new_context_with_model(model, llama_context_params_from_gpt_params(params));
 
-    // Load state (rng, logits, embedding and kv_cache) from file
+    printf("\nsecond run: %s", params.prompt.c_str());
-    {
-        FILE *fp_read = fopen("dump_state.bin", "rb");
-        if (state_size != llama_get_state_size(ctx2)) {
-            fprintf(stderr, "\n%s : failed to validate state size\n", __func__);
-            llama_free(ctx2);
-            llama_free_model(model);
-            return 1;
-        }
 
-        const size_t ret = fread(state_mem, 1, state_size, fp_read);
+    // load state (rng, logits, embedding and kv_cache) from file
-        if (ret != state_size) {
+    {
+        std::vector<uint8_t> state_mem(llama_get_state_size(ctx2));
+
+        FILE * fp_read = fopen("dump_state.bin", "rb");
+
+        const size_t ret = fread(state_mem.data(), 1, state_mem.size(), fp_read);
+        if (ret != state_mem.size()) {
             fprintf(stderr, "\n%s : failed to read state\n", __func__);
             llama_free(ctx2);
             llama_free_model(model);
             return 1;
         }
 
-        llama_set_state_data(ctx2, state_mem);  // could also read directly from memory mapped file
+        llama_set_state_data(ctx2, state_mem.data());
+
         fclose(fp_read);
     }
 
-    delete[] state_mem;
-
     // restore state (last tokens)
-    last_n_tokens_data = last_n_tokens_data_saved;
     n_past = n_past_saved;
 
     // second run
@@ -142,10 +130,11 @@ int main(int argc, char ** argv) {
         llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
         auto next_token = llama_sample_token(ctx2, &candidates_p);
         auto next_token_str = llama_token_to_piece(ctx2, next_token);
-        last_n_tokens_data.push_back(next_token);
 
         printf("%s", next_token_str.c_str());
-        if (llama_decode(ctx, llama_batch_get_one(&next_token, 1, n_past, 0))) {
+        result1 += next_token_str;
+
+        if (llama_decode(ctx2, llama_batch_get_one(&next_token, 1, n_past, 0))) {
             fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
             llama_free(ctx2);
             llama_free_model(model);
@@ -154,10 +143,17 @@ int main(int argc, char ** argv) {
         n_past += 1;
     }
 
-    printf("\n\n");
+    printf("\n");
 
     llama_free(ctx2);
     llama_free_model(model);
 
+    if (result0 != result1) {
+        fprintf(stderr, "\n%s : error : the 2 generations are different\n", __func__);
+        return 1;
+    }
+
+    fprintf(stderr, "\n%s : success\n", __func__);
+
     return 0;
 }

examples/server/CMakeLists.txt

@@ -6,7 +6,7 @@ install(TARGETS ${TARGET} RUNTIME)
 target_compile_definitions(${TARGET} PRIVATE
     SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
 )
-target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE common llama clip ${CMAKE_THREAD_LIBS_INIT})
 if (WIN32)
     TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
 endif()

examples/server/README.md

@@ -24,6 +24,10 @@ Command line options:
 -   `--port`: Set the port to listen. Default: `8080`.
 -   `--path`: path from which to serve static files (default examples/server/public)
 -   `--embedding`: Enable embedding extraction, Default: disabled.
+-   `-np N`, `--parallel N`: Set the number of slots for process requests (default: 1)
+-   `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled)
+-   `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load "a system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
+-   `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA.
 
 ## Build
 
@@ -106,25 +110,25 @@ node index.js
 
 ## API Endpoints
 
--   **POST** `/completion`: Given a prompt, it returns the predicted completion.
+-   **POST** `/completion`: Given a `prompt`, it returns the predicted completion.
 
     *Options:*
 
+    `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. If the prompt is a string or an array with the first element given as a string, a `bos` token is inserted in the front like `main` does.
+
     `temperature`: Adjust the randomness of the generated text (default: 0.8).
 
     `top_k`: Limit the next token selection to the K most probable tokens (default: 40).
 
     `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95).
 
-    `n_predict`: Set the number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: -1, -1 = infinity).
+    `n_predict`: Set the maximum number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: -1, -1 = infinity).
 
-    `n_keep`: Specify the number of tokens from the initial prompt to retain when the model resets its internal context.
+    `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded.
-    By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the initial prompt.
+    By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the prompt.
 
     `stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
 
-    `prompt`: Provide a prompt as a string, or as an array of strings and numbers representing tokens. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate. If the prompt is a string, or an array with the first element given as a string, a space is inserted in the front like main.cpp does.
-
     `stop`: Specify a JSON array of stopping strings.
     These words will not be included in the completion, so make sure to add them to the prompt for the next iteration (default: []).
 
@@ -158,6 +162,44 @@ node index.js
 
     `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token (default: 0)
 
+    `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `prompt`. You can determine the place of the image in the prompt as in the following: `USER:[img-12]Describe the image in detail.\nASSISTANT:` In this case, `[img-12]` will be replaced by the embeddings of the image id 12 in the following `image_data` array: `{..., "image_data": [{"data": "<BASE64_STRING>", "id": 12}]}`. Use `image_data` only with multimodal models, e.g., LLaVA.
+
+    *Result JSON:*
+
+    Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion.
+
+    `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string.
+
+    `stop`: Boolean for use with `stream` to check whether the generation has stopped (Note: This is not related to stopping words array `stop` from input options)
+
+    `generation_settings`: The provided options above excluding `prompt` but including `n_ctx`, `model`
+
+    `model`: The path to the model loaded with `-m`
+
+    `prompt`: The provided `prompt`
+
+    `stopped_eos`: Indicating whether the completion has stopped because it encountered the EOS token
+
+    `stopped_limit`: Indicating whether the completion stopped because `n_predict` tokens were generated before stop words or EOS was encountered
+
+    `stopped_word`: Indicating whether the completion stopped due to encountering a stopping word from `stop` JSON array provided
+
+    `stopping_word`: The stopping word encountered which stopped the generation (or "" if not stopped due to a stopping word)
+
+    `timings`: Hash of timing information about the completion such as the number of tokens `predicted_per_second`
+
+    `tokens_cached`: Number of tokens from the prompt which could be re-used from previous completion (`n_past`)
+
+    `tokens_evaluated`: Number of tokens evaluated in total from the prompt
+
+    `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`)
+
+    `slot_id`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot (default: -1)
+
+    `cache_prompt`: Save the prompt and generation for avoid reprocess entire prompt if a part of this isn't change (default: false)
+
+    `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime)
+
 -   **POST** `/tokenize`: Tokenize a given text.
 
     *Options:*
@@ -188,8 +230,32 @@ node index.js
 
     It also accepts all the options of `/completion` except `stream` and `prompt`.
 
+-   **GET** `/props`: Return the required assistant name and anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
+
 ## More examples
 
+### Change system prompt on runtime
+
+To use the server example to serve multiple chat-type clients while keeping the same system prompt, you can utilize the option `system_prompt` to achieve that. This only needs to be done once to establish it.
+
+`prompt`: Specify a context that you want all connecting clients to respect.
+
+`anti_prompt`: Specify the word you want to use to instruct the model to stop. This must be sent to each client through the `/props` endpoint.
+
+`assistant_name`: The bot's name is necessary for each customer to generate the prompt. This must be sent to each client through the `/props` endpoint.
+
+```json
+{
+    "system_prompt": {
+        "prompt": "Transcript of a never ending dialog, where the User interacts with an Assistant.\nThe Assistant is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.\nUser: Recommend a nice restaurant in the area.\nAssistant: I recommend the restaurant \"The Golden Duck\". It is a 5 star restaurant with a great view of the city. The food is delicious and the service is excellent. The prices are reasonable and the portions are generous. The restaurant is located at 123 Main Street, New York, NY 10001. The phone number is (212) 555-1234. The hours are Monday through Friday from 11:00 am to 10:00 pm. The restaurant is closed on Saturdays and Sundays.\nUser: Who is Richard Feynman?\nAssistant: Richard Feynman was an American physicist who is best known for his work in quantum mechanics and particle physics. He was awarded the Nobel Prize in Physics in 1965 for his contributions to the development of quantum electrodynamics. He was a popular lecturer and author, and he wrote several books, including \"Surely You're Joking, Mr. Feynman!\" and \"What Do You Care What Other People Think?\".\nUser:",
+        "anti_prompt": "User:",
+        "assistant_name": "Assistant:"
+    }
+}
+```
+
+**NOTE**: You can do this automatically when starting the server by simply creating a .json file with these options and using the CLI option `-spf FNAME` or `--system-prompt-file FNAME`.
+
 ### Interactive mode
 
 Check the sample in [chat.mjs](chat.mjs).

examples/server/api_like_OAI.py

@@ -8,6 +8,7 @@ import json
 
 
 app = Flask(__name__)
+slot_id = -1
 
 parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.")
 parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')
@@ -77,7 +78,8 @@ def make_postData(body, chat=False, stream=False):
     if(is_present(body, "stop")): postData["stop"] += body["stop"]
     postData["n_keep"] = -1
     postData["stream"] = stream
-
+    postData["cache_prompt"] = True
+    postData["slot_id"] = slot_id
     return postData
 
 def make_resData(data, chat=False, promptToken=[]):
@@ -128,6 +130,7 @@ def make_resData_stream(data, chat=False, time_now = 0, start=False):
             }
         ]
     }
+    slot_id = data["slot_id"]
     if (chat):
         if (start):
             resData["choices"][0]["delta"] =  {

examples/server/chat.mjs

@@ -7,6 +7,11 @@ const args = process.argv.slice(2);
 const grammarJsonSchemaFile = args.find(
     (_, index) => args[index - 1] === "--grammar-json-schema"
 );
+
+const no_cached_prompt = args.find(
+    (_, index) => args[index - 1] === "--no-cache-prompt"
+) ?? "false";
+
 const grammarFile = args.find((_, index) => args[index - 1] === "--grammar");
 
 // Example usage: function,arguments
@@ -30,6 +35,9 @@ if (grammarFile) {
     grammar = readFileSync(grammarFile, 'utf-8')
 }
 
+// for cached prompt
+let slot_id = -1;
+
 const API_URL = 'http://127.0.0.1:8080'
 
 const chat = [
@@ -76,6 +84,8 @@ async function chat_completion(question) {
             top_p: 0.9,
             n_keep: n_keep,
             n_predict: 256,
+            cache_prompt: no_cached_prompt === "false",
+            slot_id: slot_id,
             stop: ["\n### Human:"], // stop completion after generating this
             grammar,
             stream: true,
@@ -92,6 +102,7 @@ async function chat_completion(question) {
         const t = Buffer.from(chunk).toString('utf8')
         if (t.startsWith('data: ')) {
             const message = JSON.parse(t.substring(6))
+            slot_id = message.slot_id
             answer += message.content
             process.stdout.write(message.content)
             if (message.stop) {

examples/server/public/index.html

@@ -125,6 +125,7 @@
       background-color: #222;
       color: #ddd;
     }
+
     code {
       font-family: monospace;
       padding: 0.1em 0.3em;
@@ -136,7 +137,13 @@
       display: block;
     }
 
-    header, footer {
+    fieldset label.slim {
+      margin: 0 0.5em;
+      display: inline;
+    }
+
+    header,
+    footer {
       text-align: center;
     }
 
@@ -145,11 +152,20 @@
       color: #888;
     }
 
+    .mode-chat textarea[name=prompt] {
+      height: 4.5em;
+    }
+
+    .mode-completion textarea[name=prompt] {
+      height: 10em;
+    }
+
 
     @keyframes loading-bg-wipe {
       0% {
         background-position: 0%;
       }
+
       100% {
         background-position: 100%;
       }
@@ -168,6 +184,7 @@
         --loading-color-1: #22222200;
         --loading-color-2: #222222ff;
       }
+
       .popover-content {
         background-color: black;
       }
@@ -181,15 +198,18 @@
 
     import { llama } from '/completion.js';
     import { SchemaConverter } from '/json-schema-to-grammar.mjs';
+    let selected_image = false;
+    var slot_id = -1;
 
     const session = signal({
       prompt: "This is a conversation between User and Llama, a friendly chatbot. Llama is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision.",
       template: "{{prompt}}\n\n{{history}}\n{{char}}:",
       historyTemplate: "{{name}}: {{message}}",
       transcript: [],
-      type: "chat",
+      type: "chat",  // "chat" | "completion"
       char: "Llama",
       user: "User",
+      image_selected: ''
     })
 
     const params = signal({
@@ -207,7 +227,9 @@
       mirostat_tau: 5, // target entropy
       mirostat_eta: 0.1, // learning rate
       grammar: '',
-      n_probs: 0, // no completion_probabilities
+      n_probs: 0, // no completion_probabilities,
+      image_data: [],
+      cache_prompt: true
     })
 
     /* START: Support for storing prompt templates and parameters in borwser LocalStorage */
@@ -257,6 +279,7 @@
       // saved templates were successfuly imported.
 
       console.log('Processing saved templates and updating default template')
+      params.value = { ...params.value, image_data: [] };
 
       //console.log(importedTemplates);
       savedUserTemplates.value = importedTemplates;
@@ -281,7 +304,9 @@
 
     function userTemplateApply(t) {
       session.value = t.data.session;
+      session.value = { ...session.value, image_selected: '' };
       params.value = t.data.params;
+      params.value = { ...params.value, image_data: [] };
     }
 
     function userTemplateResetToDefaultAndApply() {
@@ -365,17 +390,53 @@
       return String(str).replaceAll(/\{\{(.*?)\}\}/g, (_, key) => template(settings[key]));
     }
 
+    async function runLlama(prompt, llamaParams, char) {
+      const currentMessages = [];
+      const history = session.value.transcript;
+      if (controller.value) {
+        throw new Error("already running");
+      }
+      controller.value = new AbortController();
+      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
+        const data = chunk.data;
+
+        if (data.stop) {
+          while (
+            currentMessages.length > 0 &&
+            currentMessages[currentMessages.length - 1].content.match(/\n$/) != null
+          ) {
+            currentMessages.pop();
+          }
+          transcriptUpdate([...history, [char, currentMessages]])
+          console.log("Completion finished: '", currentMessages.map(msg => msg.content).join(''), "', summary: ", data);
+        } else {
+          currentMessages.push(data);
+          slot_id = data.slot_id;
+          if (selected_image && !data.multimodal) {
+            alert("The server was not compiled for multimodal or the model projector can't be loaded.");
+            return;
+          }
+          transcriptUpdate([...history, [char, currentMessages]])
+        }
+
+        if (data.timings) {
+          llamaStats.value = data.timings;
+        }
+      }
+
+      controller.value = null;
+    }
+
     // send message to server
     const chat = async (msg) => {
       if (controller.value) {
         console.log('already running...');
         return;
       }
-      controller.value = new AbortController();
 
       transcriptUpdate([...session.value.transcript, ["{{user}}", msg]])
 
-      const prompt = template(session.value.template, {
+      let prompt = template(session.value.template, {
         message: msg,
         history: session.value.transcript.flatMap(
           ([name, data]) =>
@@ -390,56 +451,67 @@
             )
         ).join("\n"),
       });
-
+      if (selected_image) {
-      const currentMessages = [];
+        prompt = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:[img-10]${msg}\nASSISTANT:`;
-      const history = session.value.transcript
+      }
-
+      await runLlama(prompt, {
-      const llamaParams = {
         ...params.value,
+        slot_id: slot_id,
         stop: ["</s>", template("{{char}}:"), template("{{user}}:")],
+      }, "{{char}}");
+    }
+
+    const runCompletion = async () => {
+      if (controller.value) {
+        console.log('already running...');
+        return;
       }
+      const { prompt } = session.value;
+      transcriptUpdate([...session.value.transcript, ["", prompt]]);
+      await runLlama(prompt, {
+        ...params.value,
+        slot_id: slot_id,
+        stop: [],
+      }, "");
+    }
 
-      for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
+    const stop = (e) => {
-        const data = chunk.data;
+      e.preventDefault();
-
+      if (controller.value) {
-        if (data.stop) {
+        controller.value.abort();
-          while (
+        controller.value = null;
-            currentMessages.length > 0 &&
-            currentMessages[currentMessages.length - 1].content.match(/\n$/) != null
-          ) {
-            currentMessages.pop();
-          }
-          transcriptUpdate([...history, ["{{char}}", currentMessages]])
-          console.log("Completion finished: '", currentMessages.map(msg => msg.content).join(''), "', summary: ", data);
-        } else {
-          currentMessages.push(data);
-          transcriptUpdate([...history, ["{{char}}", currentMessages]])
-        }
-
-        if (data.timings) {
-          llamaStats.value = data.timings;
-        }
       }
+    }
 
-      controller.value = null;
+    const reset = (e) => {
+      stop(e);
+      transcriptUpdate([]);
+    }
+
+    const uploadImage = (e) => {
+      e.preventDefault();
+      document.getElementById("fileInput").click();
+      document.getElementById("fileInput").addEventListener("change", function (event) {
+        const selectedFile = event.target.files[0];
+        if (selectedFile) {
+          const reader = new FileReader();
+          reader.onload = function () {
+            const image_data = reader.result;
+            session.value = { ...session.value, image_selected: image_data };
+            params.value = {
+              ...params.value, image_data: [
+                { data: image_data.replace(/data:image\/[^;]+;base64,/, ''), id: 10 }]
+            }
+          };
+          selected_image = true;
+          reader.readAsDataURL(selectedFile);
+        }
+      });
     }
 
     function MessageInput() {
       const message = useSignal("")
 
-      const stop = (e) => {
-        e.preventDefault();
-        if (controller.value) {
-          controller.value.abort();
-          controller.value = null;
-        }
-      }
-
-      const reset = (e) => {
-        stop(e);
-        transcriptUpdate([]);
-      }
-
       const submit = (e) => {
         stop(e);
         chat(message.value);
@@ -467,6 +539,7 @@
           </div>
           <div class="right">
             <button type="submit" disabled=${generating.value}>Send</button>
+            <button onclick=${uploadImage}>Upload Image</button>
             <button onclick=${stop} disabled=${!generating.value}>Stop</button>
             <button onclick=${reset}>Reset</button>
           </div>
@@ -474,6 +547,19 @@
       `
     }
 
+    function CompletionControls() {
+      const submit = (e) => {
+        stop(e);
+        runCompletion();
+      }
+      return html`
+        <div>
+          <button onclick=${submit} type="button" disabled=${generating.value}>Start</button>
+          <button onclick=${stop} disabled=${!generating.value}>Stop</button>
+          <button onclick=${reset}>Reset</button>
+        </div>`;
+    }
+
     const ChatLog = (props) => {
       const messages = session.value.transcript;
       const container = useRef(null)
@@ -497,11 +583,16 @@
             data;
           message = html`<${Markdownish} text=${template(text)} />`
         }
-        return html`<p key=${index}><strong>${template(user)}:</strong> ${message}</p>`
+        if (user) {
+          return html`<p key=${index}><strong>${template(user)}:</strong> ${message}</p>`
+        } else {
+          return html`<p key=${index}>${message}</p>`
+        }
       };
 
       return html`
         <section id="chat" ref=${container}>
+          <img style="width: 60%;${!session.value.image_selected ? `display: none;` : ``}" src="${session.value.image_selected}"/>
           ${messages.flatMap(chatLine)}
         </section>`;
     };
@@ -520,7 +611,7 @@
           const converter = new SchemaConverter(
             grammarJsonSchemaPropOrder.value
               .split(',')
-              .reduce((acc, cur, i) => ({...acc, [cur.trim()]: i}), {})
+              .reduce((acc, cur, i) => ({ ...acc, [cur.trim()]: i }), {})
           )
           converter.visit(schema, '')
           params.value = {
@@ -532,7 +623,7 @@
         }
       }
 
-      const FloatField = ({label, max, min, name, step, value}) => {
+      const FloatField = ({ label, max, min, name, step, value }) => {
         return html`
           <div>
             <label for="${name}">${label}</label>
@@ -542,7 +633,7 @@
         `
       };
 
-      const IntField = ({label, max, min, name, value}) => {
+      const IntField = ({ label, max, min, name, value }) => {
         return html`
           <div>
             <label for="${name}">${label}</label>
@@ -574,18 +665,31 @@
         userTemplateAutosave()
       }, [session.value, params.value])
 
-      return html`
+      const GrammarControl = () => (
-        <form>
+        html`
-          <fieldset>
+          <div>
-            <${UserTemplateResetButton}/>
+            <label for="template">Grammar</label>
-          </fieldset>
+            <textarea id="grammar" name="grammar" placeholder="Use gbnf or JSON Schema+convert" value="${params.value.grammar}" rows=4 oninput=${updateParams}/>
+            <input type="text" name="prop-order" placeholder="order: prop1,prop2,prop3" oninput=${updateGrammarJsonSchemaPropOrder} />
+            <button type="button" onclick=${convertJSONSchemaGrammar}>Convert JSON Schema</button>
+          </div>
+          `
+      );
 
-          <fieldset>
+      const PromptControlFieldSet = () => (
-            <div>
+        html`
-              <label for="prompt">Prompt</label>
+        <fieldset>
-              <textarea type="text" name="prompt" value="${session.value.prompt}" rows=4 oninput=${updateSession}/>
+          <div>
-            </div>
+            <label htmlFor="prompt">Prompt</label>
-          </fieldset>
+            <textarea type="text" name="prompt" value="${session.value.prompt}" oninput=${updateSession}/>
+          </div>
+        </fieldset>
+        `
+      );
+
+      const ChatConfigForm = () => (
+        html`
+          ${PromptControlFieldSet()}
 
           <fieldset class="two">
             <div>
@@ -609,30 +713,45 @@
               <label for="template">Chat history template</label>
               <textarea id="template" name="historyTemplate" value="${session.value.historyTemplate}" rows=1 oninput=${updateSession}/>
             </div>
+            ${GrammarControl()}
+          </fieldset>
+      `
+      );
 
+      const CompletionConfigForm = () => (
+        html`
+          ${PromptControlFieldSet()}
+          <fieldset>${GrammarControl()}</fieldset>
+        `
+      );
+
+      return html`
+        <form>
+          <fieldset class="two">
+            <${UserTemplateResetButton}/>
             <div>
-              <label for="template">Grammar</label>
+              <label class="slim"><input type="radio" name="type" value="chat" checked=${session.value.type === "chat"} oninput=${updateSession} /> Chat</label>
-              <textarea id="grammar" name="grammar" placeholder="Use gbnf or JSON Schema+convert" value="${params.value.grammar}" rows=4 oninput=${updateParams}/>
+              <label class="slim"><input type="radio" name="type" value="completion" checked=${session.value.type === "completion"} oninput=${updateSession} /> Completion</label>
-              <input type="text" name="prop-order" placeholder="order: prop1,prop2,prop3" oninput=${updateGrammarJsonSchemaPropOrder} />
-              <button type="button" onclick=${convertJSONSchemaGrammar}>Convert JSON Schema</button>
             </div>
           </fieldset>
 
+          ${session.value.type === 'chat' ? ChatConfigForm() : CompletionConfigForm()}
+
           <fieldset class="two">
-            ${IntField({label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict})}
+            ${IntField({ label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict })}
-            ${FloatField({label: "Temperature", max: 1.5, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature})}
+            ${FloatField({ label: "Temperature", max: 1.5, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })}
-            ${FloatField({label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty})}
+            ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })}
-            ${IntField({label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n})}
+            ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })}
-            ${IntField({label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k})}
+            ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })}
-            ${FloatField({label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p})}
+            ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })}
           </fieldset>
           <details>
             <summary>More options</summary>
             <fieldset class="two">
-              ${FloatField({label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z})}
+              ${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })}
-              ${FloatField({label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p})}
+              ${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })}
-              ${FloatField({label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty})}
+              ${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })}
-              ${FloatField({label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty})}
+              ${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })}
             </fieldset>
             <hr />
             <fieldset class="three">
@@ -641,11 +760,11 @@
                 <label><input type="radio" name="mirostat" value="1" checked=${params.value.mirostat == 1} oninput=${updateParamsInt} /> Mirostat v1</label>
                 <label><input type="radio" name="mirostat" value="2" checked=${params.value.mirostat == 2} oninput=${updateParamsInt} /> Mirostat v2</label>
               </div>
-              ${FloatField({label: "Mirostat tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau})}
+              ${FloatField({ label: "Mirostat tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau })}
-              ${FloatField({label: "Mirostat eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta})}
+              ${FloatField({ label: "Mirostat eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta })}
             </fieldset>
             <fieldset>
-              ${IntField({label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs})}
+              ${IntField({ label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs })}
             </fieldset>
           </details>
         </form>
@@ -684,20 +803,20 @@
         const popoverChildren = html`
           <div class="prob-set">
             ${probs.map((p, index) => {
-              return html`
+          return html`
                 <div
                   key=${index}
                   title=${`prob: ${p.prob}`}
                   style=${{
-                    padding: '0.3em',
+              padding: '0.3em',
-                    backgroundColor: p.tok_str === content ? probColor(p.prob) : 'transparent'
+              backgroundColor: p.tok_str === content ? probColor(p.prob) : 'transparent'
-                  }}
+            }}
                 >
                   <span>${p.tok_str}: </span>
                   <span>${Math.floor(p.prob * 100)}%</span>
                 </div>
               `
-            })}
+        })}
           </div>
         `
 
@@ -776,9 +895,9 @@
               ref=${popoverRef}
               class="popover-content"
               style=${{
-                top: position.value.top,
+            top: position.value.top,
-                left: position.value.left,
+            left: position.value.left,
-              }}
+          }}
             >
               ${props.popoverChildren}
             </div>
@@ -851,7 +970,7 @@
     function App(props) {
 
       return html`
-        <div>
+        <div class="mode-${session.value.type}">
           <header>
             <h1>llama.cpp</h1>
           </header>
@@ -861,7 +980,7 @@
           </main>
 
           <section id="write">
-            <${MessageInput} />
+            <${session.value.type === 'chat' ? MessageInput : CompletionControls} />
           </section>
 
           <footer>
@@ -877,8 +996,11 @@
 </head>
 
 <body>
-  <div id="container"></div>
+  <div id="container">
+    <input type="file" id="fileInput" accept="image/*" style="display: none;">
+  </div>
   <div id="portal"></div>
 </body>
 
 </html>
+

examples/simple/simple.cpp

@@ -92,7 +92,7 @@ int main(int argc, char ** argv) {
     // create a llama_batch with size 512
     // we use this object to submit token data for decoding
 
-    llama_batch batch = llama_batch_init(512, 0);
+    llama_batch batch = llama_batch_init(512, 0, 1);
 
     // evaluate the initial prompt
     batch.n_tokens = tokens_list.size();
@@ -138,7 +138,7 @@ int main(int argc, char ** argv) {
             const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
 
             // is it an end of stream?
-            if (new_token_id == llama_token_eos(ctx) || n_cur == n_len) {
+            if (new_token_id == llama_token_eos(model) || n_cur == n_len) {
                 LOG_TEE("\n");
 
                 break;

examples/speculative/speculative.cpp

@@ -2,13 +2,25 @@
 
 #include "common.h"
 #include "llama.h"
-#include "grammar-parser.h"
 
 #include <cmath>
 #include <cstdio>
 #include <string>
 #include <vector>
 
+struct seq_draft {
+    bool active   = false;
+    bool drafting = false;
+    bool skip     = false;
+
+    int i_batch_dft = 0;
+    std::vector<int> i_batch_tgt;
+
+    std::vector<llama_token> tokens;
+
+    struct llama_sampling_context * ctx_sampling;
+};
+
 int main(int argc, char ** argv) {
     gpt_params params;
 
@@ -21,6 +33,13 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
+    // max number of parallel drafting sequences (i.e. tree branches)
+    const int n_seq_dft = params.n_parallel;
+
+    // TODO: make this configurable
+    const float p_accept = 0.80f;
+    const float p_split  = 0.10f;
+
 #ifndef LOG_DISABLE_LOGS
     log_set_target(log_filename_generator("speculative", "log"));
     LOG_TEE("Log start\n");
@@ -77,8 +96,6 @@ int main(int argc, char ** argv) {
     const auto t_enc_end = ggml_time_us();
 
     // the 2 models should have the same vocab
-    const int n_ctx   = llama_n_ctx(ctx_tgt);
-    const int n_vocab = llama_n_vocab(model_tgt);
     //GGML_ASSERT(n_vocab == llama_n_vocab(model_dft));
 
     // how many tokens to draft each time
@@ -91,114 +108,128 @@ int main(int argc, char ** argv) {
     int n_past_tgt = inp.size();
     int n_past_dft = inp.size();
 
-    std::vector<llama_token> drafted;
-
-    std::vector<llama_token> last_tokens(n_ctx);
-    std::fill(last_tokens.begin(), last_tokens.end(), 0);
-
-    for (auto & id : inp) {
-        last_tokens.erase(last_tokens.begin());
-        last_tokens.push_back(id);
-    }
-
-    std::vector<llama_token_data> candidates;
-    candidates.reserve(n_vocab);
-
     // used to determine end of generation
     bool has_eos = false;
 
-    // grammar stuff
+    // target model sampling context
-    struct llama_grammar * grammar_dft = NULL;
+    struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams);
-    struct llama_grammar * grammar_tgt = NULL;
 
-    grammar_parser::parse_state parsed_grammar;
+    // draft sequence data
+    std::vector<seq_draft> drafts(n_seq_dft);
 
-    // if requested - load the grammar, error checking is omitted for brevity
+    params.sparams.grammar.clear(); // the draft samplers will copy the target sampler's grammar
-    if (!params.grammar.empty()) {
+    params.sparams.temp = std::max(0.01f, params.sparams.temp);
-        parsed_grammar = grammar_parser::parse(params.grammar.c_str());
-        // will be empty (default) if there are parse errors
-        if (parsed_grammar.rules.empty()) {
-            return 1;
-        }
 
-        std::vector<const llama_grammar_element *> grammar_rules(parsed_grammar.c_rules());
+    for (int s = 0; s < n_seq_dft; ++s) {
-        grammar_tgt = llama_grammar_init(grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root"));
+        drafts[s].ctx_sampling = llama_sampling_init(params.sparams);
     }
 
+    llama_batch batch_dft = llama_batch_init(params.n_ctx, 0, 1);
+    llama_batch batch_tgt = llama_batch_init(params.n_ctx, 0, n_seq_dft);
+
     const auto t_dec_start = ggml_time_us();
 
-    while (true) {
+    // sample from the last token of the prompt
-        LOG("drafted: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_dft, drafted));
+    drafts[0].i_batch_tgt.resize(1);
+    drafts[0].i_batch_tgt[0] = 0;
 
-        int i_dft = 0;
+    while (true) {
+        // print current draft sequences
+        for (int s = 0; s < n_seq_dft; ++s) {
+            if (!drafts[s].active) {
+                continue;
+            }
+
+            const auto & tokens = drafts[s].tokens;
+
+            LOG("draft %d: %s\n", s, LOG_TOKENS_TOSTR_PRETTY(ctx_dft, tokens).c_str());
+        }
+
+        int i_dft  = 0;
+        int s_keep = 0;
 
         while (true) {
+            LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]);
+
             // sample from the target model
-            llama_token id = llama_sample_token(ctx_tgt, NULL, grammar_tgt, params, last_tokens, candidates, i_dft);
+            llama_token id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]);
 
-            // remember which tokens were sampled - used for repetition penalties during sampling
+            llama_sampling_accept(ctx_sampling, ctx_tgt, id, true);
-            last_tokens.erase(last_tokens.begin());
-            last_tokens.push_back(id);
 
-            //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, last_tokens));
+            //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str());
 
             const std::string token_str = llama_token_to_piece(ctx_tgt, id);
+
             printf("%s", token_str.c_str());
             fflush(stdout);
 
-            if (id == llama_token_eos(ctx_tgt)) {
+            if (id == llama_token_eos(model_tgt)) {
                 has_eos = true;
             }
 
             ++n_predict;
 
-            // check if the draft matches the target
+            // check if the target token matches any of the drafts
-            if (i_dft < (int) drafted.size() && id == drafted[i_dft]) {
-                LOG("the sampled target token matches the %dth drafted token (%d, '%s') - accepted\n", i_dft, id, token_str.c_str());
-                ++n_accept;
-                ++n_past_tgt;
-                ++n_past_dft;
-                ++i_dft;
-
-                continue;
-            }
-
-            // the drafted token was rejected or we are out of drafted tokens
-
-            if (i_dft < (int) drafted.size()) {
-                LOG("the %dth drafted token (%d, '%s') does not match the sampled target token (%d, '%s') - rejected\n",
-                        i_dft, drafted[i_dft], llama_token_to_piece(ctx_dft, drafted[i_dft]).c_str(), id, token_str.c_str());
-            } else {
-                LOG("out of drafted tokens\n");
-            }
-
-            llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1);
-            llama_decode(ctx_dft, llama_batch_get_one(&id, 1, n_past_dft, 0));
-            ++n_past_dft;
-
-            // heuristic for n_draft
             {
-                const int  n_draft_cur  = (int) drafted.size();
+                bool matches = false;
-                const bool all_accepted = i_dft == n_draft_cur;
 
-                LOG("n_draft      = %d\n", n_draft);
+                for (int s = 0; s < n_seq_dft; ++s) {
-                LOG("n_draft_cur  = %d\n", n_draft_cur);
+                    if (!drafts[s].active) {
-                LOG("i_dft        = %d\n", i_dft);
+                        continue;
-                LOG("all_accepted = %d\n", all_accepted);
+                    }
 
-                if (all_accepted && n_draft == n_draft_cur) {
+                    if (i_dft < (int) drafts[s].tokens.size() && id == drafts[s].tokens[i_dft]) {
-                    LOG(" - max drafted tokens accepted - n_draft += 8\n");
+                        LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, id, token_str.c_str());
-                    n_draft = std::min(30, n_draft + 8);
+
-                } else if (all_accepted) {
+                        s_keep = s;
-                    LOG(" - partially drafted tokens accepted - no change\n");
+                        matches = true;
-                } else {
+                    } else {
-                    LOG(" - drafted token rejected - n_draft -= 1\n");
+                        drafts[s].active = false;
-                    n_draft = std::max(2, n_draft - 1);
+                    }
+                }
+
+                if (matches) {
+                    ++n_accept;
+                    ++n_past_tgt;
+                    ++n_past_dft;
+                    ++i_dft;
+
+                    continue;
                 }
             }
 
-            drafted.clear();
+            LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str());
-            drafted.push_back(id);
+
+            // TODO: simplify
+            {
+                LOG("keeping sequence %d, n_past_tgt = %d, n_past_dft = %d\n", s_keep, n_past_tgt, n_past_dft);
+
+                llama_kv_cache_seq_keep(ctx_dft, s_keep);
+                llama_kv_cache_seq_cp  (ctx_dft, s_keep, 0, -1, -1);
+                llama_kv_cache_seq_keep(ctx_dft, 0);
+
+                llama_kv_cache_seq_rm  (ctx_tgt, s_keep, n_past_tgt, -1);
+                llama_kv_cache_seq_keep(ctx_tgt, s_keep);
+                llama_kv_cache_seq_cp  (ctx_tgt, s_keep, 0, -1, -1);
+                llama_kv_cache_seq_keep(ctx_tgt, 0);
+            }
+
+            for (int s = 0; s < n_seq_dft; ++s) {
+                drafts[s].active = false;
+                drafts[s].tokens.clear();
+                drafts[s].i_batch_tgt.clear();
+            }
+            // note: will be erased after the speculation phase
+            drafts[0].tokens.push_back(id);
+            drafts[0].i_batch_tgt.push_back(0);
+
+            llama_batch_clear(batch_dft);
+            llama_batch_add  (batch_dft, id, n_past_dft, { 0 }, true);
+
+            llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1);
+            llama_decode         (ctx_dft, batch_dft);
+
+            ++n_past_dft;
 
             break;
         }
@@ -207,72 +238,151 @@ int main(int argc, char ** argv) {
             break;
         }
 
-        if (grammar_tgt) {
+        llama_sampling_cp(ctx_sampling, drafts[0].ctx_sampling);
-            if (grammar_dft) {
-                llama_grammar_free(grammar_dft);
-            }
-            grammar_dft = llama_grammar_copy(grammar_tgt);
 
-            LOG("copied target grammar to draft grammar\n");
+        int n_seq_cur  = 1;
-        }
-
-        // sample n_draft tokens from the draft model using greedy decoding
         int n_past_cur = n_past_dft;
+
+        for (int s = 0; s < n_seq_dft; ++s) {
+            drafts[s].active   = false;
+            drafts[s].drafting = false;
+        }
+        drafts[0].active      = true;
+        drafts[0].drafting    = true;
+        drafts[0].i_batch_dft = 0;
+
+        llama_batch_clear(batch_tgt);
+        llama_batch_add  (batch_tgt, drafts[0].tokens[0], n_past_tgt, { 0 }, true);
+
+        // sample n_draft tokens from the draft model using tree-based sampling
         for (int i = 0; i < n_draft; ++i) {
-            float * logits = llama_get_logits(ctx_dft);
+            batch_dft.n_tokens = 0;
 
-            candidates.clear();
+            for (int s = 0; s < n_seq_dft; ++s) {
-            for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
+                drafts[s].skip = false;
-                candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
             }
 
-            llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
+            for (int s = 0; s < n_seq_dft; ++s) {
+                if (!drafts[s].drafting || drafts[s].skip) {
+                    continue;
+                }
 
-            if (grammar_dft != NULL) {
+                llama_sampling_sample(drafts[s].ctx_sampling, ctx_dft, NULL, drafts[s].i_batch_dft);
-                llama_sample_grammar(ctx_dft, &cur_p, grammar_dft);
+
+                const auto & cur_p = drafts[s].ctx_sampling->cur;
+
+                for (int k = 0; k < std::min(n_seq_dft + 3, (int) cur_p.size()); ++k) {
+                    LOG(" - draft candidate %3d for seq %3d, pos %3d: %6d (%8.3f) '%s'\n",
+                            k, s, i, cur_p[k].id, cur_p[k].p, llama_token_to_piece(ctx_dft, cur_p[k].id).c_str());
+                }
+
+                if (cur_p[0].p < p_accept) {
+                    LOG("stopping drafting for seq %3d, probability too low: %.3f < %.3f\n", s, cur_p[0].p, p_accept);
+                    drafts[s].drafting = false;
+                    continue;
+                }
+
+                std::vector<int> sa(1, s);
+
+                // attempt to split the branch if the probability is high enough
+                for (int f = 1; f < 8; ++f) {
+                    if (n_seq_cur < n_seq_dft && cur_p[f].p > p_split) {
+                        LOG("splitting seq %3d into %3d\n", s, n_seq_cur);
+
+                        llama_kv_cache_seq_rm(ctx_dft,    n_seq_cur, -1, -1);
+                        llama_kv_cache_seq_cp(ctx_dft, s, n_seq_cur, -1, -1);
+
+                        // all previous tokens from this branch are now also part of the new branch
+                        for (int t = 0; t < batch_tgt.n_tokens; ++t) {
+                            for (int p = 0; p < batch_tgt.n_seq_id[t]; ++p) {
+                                if (batch_tgt.seq_id[t][p] == s) {
+                                    batch_tgt.seq_id[t][batch_tgt.n_seq_id[t]] = n_seq_cur;
+                                    batch_tgt.n_seq_id[t]++;
+                                    break;
+                                }
+                            }
+                        }
+
+                        // copy the draft state
+                        drafts[n_seq_cur].active   = true;
+                        drafts[n_seq_cur].drafting = true;
+                        drafts[n_seq_cur].skip     = true;
+
+                        drafts[n_seq_cur].tokens      = drafts[s].tokens;
+                        drafts[n_seq_cur].i_batch_dft = drafts[s].i_batch_dft;
+                        drafts[n_seq_cur].i_batch_tgt = drafts[s].i_batch_tgt;
+
+                        llama_sampling_cp(drafts[s].ctx_sampling, drafts[n_seq_cur].ctx_sampling);
+
+                        sa.push_back(n_seq_cur);
+
+                        n_seq_cur++;
+                    } else {
+                        break;
+                    }
+                }
+
+                // add drafted token for each sequence
+                for (int is = 0; is < (int) sa.size(); ++is) {
+                    const llama_token id = cur_p[is].id;
+
+                    const int s = sa[is];
+
+                    llama_sampling_accept(drafts[s].ctx_sampling, ctx_dft, id, true);
+
+                    drafts[s].tokens.push_back(id);
+
+                    // add unique drafted tokens to the target batch
+                    drafts[s].i_batch_tgt.push_back(batch_tgt.n_tokens);
+
+                    llama_batch_add(batch_tgt, id, n_past_tgt + i + 1, { s }, true);
+
+                    // add the token to the batch for batched decoding with the draft model
+                    drafts[s].i_batch_dft = batch_dft.n_tokens;
+
+                    llama_batch_add(batch_dft, id, n_past_cur, { s }, true);
+
+                    if (batch_tgt.n_tokens > n_draft) {
+                        drafts[s].drafting = false;
+                    }
+                }
             }
 
-            // computes softmax and sorts the candidates
+            // no sequence is drafting anymore
-            llama_sample_softmax(ctx_dft, &cur_p);
+            if (batch_dft.n_tokens == 0) {
-
-            for (int i = 0; i < 3; ++i) {
-                LOG(" - draft candidate %3d: %6d (%8.3f) '%s'\n", i, cur_p.data[i].id, cur_p.data[i].p, llama_token_to_piece(ctx_dft, cur_p.data[i].id).c_str());
-            }
-
-            // TODO: better logic?
-            if (cur_p.data[0].p < 2*cur_p.data[1].p) {
-                LOG("stopping drafting, probability too low: %.3f < 2*%.3f\n", cur_p.data[0].p, cur_p.data[1].p);
                 break;
             }
 
-            // drafted token
+            // evaluate the drafted tokens on the draft model
-            const llama_token id = cur_p.data[0].id;
+            llama_decode(ctx_dft, batch_dft);
-
+            ++n_past_cur;
-            drafted.push_back(id);
             ++n_drafted;
 
-            // no need to evaluate the last drafted token, since we won't use the result
+            if (batch_tgt.n_tokens > n_draft) {
-            if (i == n_draft - 1) {
                 break;
             }
-
-            // evaluate the drafted token on the draft model
-            llama_kv_cache_seq_rm(ctx_dft, 0, n_past_cur, -1);
-            llama_decode(ctx_dft, llama_batch_get_one(&drafted.back(), 1, n_past_cur, 0));
-            ++n_past_cur;
-
-            if (grammar_dft != NULL) {
-                llama_grammar_accept_token(ctx_dft, grammar_dft, id);
-            }
         }
 
         // evaluate the target model on the drafted tokens
-        llama_kv_cache_seq_rm(ctx_tgt, 0, n_past_tgt, -1);
+        {
-        llama_decode(ctx_tgt, llama_batch_get_one(drafted.data(), drafted.size(), n_past_tgt, 0));
+            llama_kv_cache_seq_keep(ctx_tgt, 0);
-        ++n_past_tgt;
+            for (int s = 1; s < n_seq_dft; ++s) {
+                llama_kv_cache_seq_cp(ctx_tgt, 0, s, -1, -1);
+            }
 
-        // the first token is always proposed by the traget model before the speculation loop
+            //LOG("target batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_tgt, batch_tgt));
-        drafted.erase(drafted.begin());
+            llama_decode(ctx_tgt, batch_tgt);
+            ++n_past_tgt;
+        }
+
+        // the first token is always proposed by the traget model before the speculation loop so we erase it here
+        for (int s = 0; s < n_seq_dft; ++s) {
+            if (!drafts[s].active) {
+                continue;
+            }
+
+            drafts[s].tokens.erase(drafts[s].tokens.begin());
+        }
     }
 
     auto t_dec_end = ggml_time_us();
@@ -280,9 +390,8 @@ int main(int argc, char ** argv) {
     LOG_TEE("\n\n");
 
     LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input,   (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
-    LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
+    LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict  / ((t_dec_end - t_dec_start) / 1e6f));
 
-    // TODO: make sure these numbers are computed correctly
     LOG_TEE("\n");
     LOG_TEE("n_draft   = %d\n", n_draft);
     LOG_TEE("n_predict = %d\n", n_predict);
@@ -296,16 +405,19 @@ int main(int argc, char ** argv) {
     LOG_TEE("\ntarget:\n");
     llama_print_timings(ctx_tgt);
 
+    llama_sampling_free(ctx_sampling);
+    for (int s = 0; s < n_seq_dft; ++s) {
+        llama_sampling_free(drafts[s].ctx_sampling);
+    }
+
+    llama_batch_free(batch_dft);
+
     llama_free(ctx_tgt);
     llama_free_model(model_tgt);
 
     llama_free(ctx_dft);
     llama_free_model(model_dft);
 
-    if (grammar_dft != NULL) {
-        llama_grammar_free(grammar_dft);
-        llama_grammar_free(grammar_tgt);
-    }
     llama_backend_free();
 
     fprintf(stderr, "\n\n");

examples/train-text-from-scratch/train-text-from-scratch.cpp

@@ -253,13 +253,14 @@ static void init_model(struct my_llama_model * model) {
     set_param_model(model);
 
     // measure data size
-    struct ggml_allocr * alloc = NULL;
+    size_t size = 0;
-    alloc = ggml_allocr_new_measure(tensor_alignment);
+    for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
-    alloc_model(alloc, model);
+        size += GGML_PAD(ggml_nbytes(t), tensor_alignment);
+    }
 
     // allocate data
-    model->data.resize(ggml_allocr_max_size(alloc) + tensor_alignment);
+    struct ggml_allocr * alloc = NULL;
-    ggml_allocr_free(alloc);
+    model->data.resize(size + tensor_alignment);
     alloc = ggml_allocr_new(model->data.data(), model->data.size(), tensor_alignment);
     alloc_model(alloc, model);
     ggml_allocr_free(alloc);
@@ -1094,11 +1095,9 @@ int main(int argc, char ** argv) {
     struct ggml_tensor * target_probs  = ggml_new_tensor_3d(ctx_input, GGML_TYPE_F32, n_vocab,  n_tokens, n_batch);
 
     // measure required memory for input tensors
-    alloc = ggml_allocr_new_measure(tensor_alignment);
+    size_t max_input_size = GGML_PAD(ggml_nbytes(tokens_input), tensor_alignment) +
-    ggml_allocr_alloc(alloc, tokens_input);
+                            GGML_PAD(ggml_nbytes(target_probs), tensor_alignment) +
-    ggml_allocr_alloc(alloc, target_probs);
+                            tensor_alignment;
-    size_t max_input_size = ggml_allocr_max_size(alloc) + tensor_alignment;
-    ggml_allocr_free(alloc);
     printf("%s: input_size = %zu bytes (%.1f MB)\n", __func__, max_input_size, (float) max_input_size / (1024.0f*1024.0f));
 
     // allocate input tensors

ggml-alloc.c

@@ -386,7 +386,7 @@ static void init_view(struct ggml_allocr * alloc, struct ggml_tensor * view) {
 
     // FIXME: the view should be initialized by the owning buffer, but currently this breaks the CUDA backend
     // due to the ggml_tensor_extra_gpu ring buffer overwriting the KV cache extras
-    assert(ggml_allocr_is_measure(alloc) || view->buffer->backend == alloc->buffer->backend);
+    assert(ggml_allocr_is_measure(alloc) || !view->buffer || view->buffer->backend == alloc->buffer->backend);
     ggml_backend_buffer_init_tensor(alloc->buffer, view);
 }
 

ggml-cuda.cu

@@ -415,6 +415,7 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_
 #define CUDA_SILU_BLOCK_SIZE 256
 #define CUDA_CPY_BLOCK_SIZE 32
 #define CUDA_SCALE_BLOCK_SIZE 256
+#define CUDA_CLAMP_BLOCK_SIZE 256
 #define CUDA_ROPE_BLOCK_SIZE 256
 #define CUDA_ALIBI_BLOCK_SIZE 32
 #define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32
@@ -4585,6 +4586,15 @@ static __global__ void scale_f32(const float * x, float * dst, const float scale
     dst[i] = scale * x[i];
 }
 
+static __global__ void clamp_f32(const float * x, float * dst, const float min, const float max, const int k) {
+    const int i = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (i >= k) {
+        return;
+    }
+
+    dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]);
+}
 
 template<int qk, int qr, dequantize_kernel_t dq>
 static void get_rows_cuda(const void * x, const int32_t * y, float * dst, const int nrows, const int ncols, cudaStream_t stream) {
@@ -5475,6 +5485,11 @@ static void scale_f32_cuda(const float * x, float * dst, const float scale, cons
     scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, k);
 }
 
+static void clamp_f32_cuda(const float * x, float * dst, const float min, const float max, const int k, cudaStream_t stream) {
+    const int num_blocks = (k + CUDA_CLAMP_BLOCK_SIZE - 1) / CUDA_CLAMP_BLOCK_SIZE;
+    clamp_f32<<<num_blocks, CUDA_CLAMP_BLOCK_SIZE, 0, stream>>>(x, dst, min, max, k);
+}
+
 template<typename T>
 static void rope_cuda(const T * x, T * dst, const int ncols, const int nrows, const int32_t * pos, const float freq_scale,
                           const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
@@ -6419,12 +6434,12 @@ inline void ggml_cuda_op_alibi(
     const int64_t ne02 = src0->ne[2];
     const int64_t nrows = ggml_nrows(src0);
 
-    const int n_past = ((int32_t *) dst->op_params)[0];
+    //const int n_past = ((int32_t *) dst->op_params)[0];
     const int n_head = ((int32_t *) dst->op_params)[1];
     float max_bias;
     memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
 
-    GGML_ASSERT(ne01 + n_past == ne00);
+    //GGML_ASSERT(ne01 + n_past == ne00);
     GGML_ASSERT(n_head == ne02);
 
     const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
@@ -6500,6 +6515,24 @@ inline void ggml_cuda_op_scale(
     (void) src1_dd;
 }
 
+inline void ggml_cuda_op_clamp(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
+    const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+
+    const float min = ((float *) dst->op_params)[0];
+    const float max = ((float *) dst->op_params)[1];
+
+    clamp_f32_cuda(src0_dd, dst_dd, min, max, ggml_nelements(src0), main_stream);
+    CUDA_CHECK(cudaGetLastError());
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
+
 static void ggml_cuda_op_flatten(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const ggml_cuda_op_flatten_t op) {
     const int64_t nrows0 = ggml_nrows(src0);
 
@@ -7061,6 +7094,10 @@ static void ggml_cuda_scale(const ggml_tensor * src0, const ggml_tensor * src1,
     ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_scale);
 }
 
+static void ggml_cuda_clamp(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_clamp);
+}
+
 static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     const int64_t ne = ggml_nelements(src0);
     GGML_ASSERT(ne == ggml_nelements(src1));
@@ -7470,6 +7507,12 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
         case GGML_OP_SCALE:
             func = ggml_cuda_scale;
             break;
+        case GGML_OP_CLAMP:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cuda_clamp;
+            break;
         case GGML_OP_CPY:
             func = ggml_cuda_cpy;
             break;

ggml-metal.m

@@ -62,6 +62,7 @@ struct ggml_metal_context {
     GGML_METAL_DECL_KERNEL(mul);
     GGML_METAL_DECL_KERNEL(mul_row); // TODO: avoid this extra kernel, instead extend the "mul" kernel to support broadcast
     GGML_METAL_DECL_KERNEL(scale);
+    GGML_METAL_DECL_KERNEL(scale_4);
     GGML_METAL_DECL_KERNEL(silu);
     GGML_METAL_DECL_KERNEL(relu);
     GGML_METAL_DECL_KERNEL(gelu);
@@ -73,6 +74,8 @@ struct ggml_metal_context {
     GGML_METAL_DECL_KERNEL(get_rows_f16);
     GGML_METAL_DECL_KERNEL(get_rows_q4_0);
     GGML_METAL_DECL_KERNEL(get_rows_q4_1);
+    GGML_METAL_DECL_KERNEL(get_rows_q5_0);
+    GGML_METAL_DECL_KERNEL(get_rows_q5_1);
     GGML_METAL_DECL_KERNEL(get_rows_q8_0);
     GGML_METAL_DECL_KERNEL(get_rows_q2_K);
     GGML_METAL_DECL_KERNEL(get_rows_q3_K);
@@ -87,6 +90,8 @@ struct ggml_metal_context {
     GGML_METAL_DECL_KERNEL(mul_mv_f16_f32_l4);
     GGML_METAL_DECL_KERNEL(mul_mv_q4_0_f32);
     GGML_METAL_DECL_KERNEL(mul_mv_q4_1_f32);
+    GGML_METAL_DECL_KERNEL(mul_mv_q5_0_f32);
+    GGML_METAL_DECL_KERNEL(mul_mv_q5_1_f32);
     GGML_METAL_DECL_KERNEL(mul_mv_q8_0_f32);
     GGML_METAL_DECL_KERNEL(mul_mv_q2_K_f32);
     GGML_METAL_DECL_KERNEL(mul_mv_q3_K_f32);
@@ -97,6 +102,8 @@ struct ggml_metal_context {
     GGML_METAL_DECL_KERNEL(mul_mm_f16_f32);
     GGML_METAL_DECL_KERNEL(mul_mm_q4_0_f32);
     GGML_METAL_DECL_KERNEL(mul_mm_q4_1_f32);
+    GGML_METAL_DECL_KERNEL(mul_mm_q5_0_f32);
+    GGML_METAL_DECL_KERNEL(mul_mm_q5_1_f32);
     GGML_METAL_DECL_KERNEL(mul_mm_q8_0_f32);
     GGML_METAL_DECL_KERNEL(mul_mm_q2_K_f32);
     GGML_METAL_DECL_KERNEL(mul_mm_q3_K_f32);
@@ -243,6 +250,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
         GGML_METAL_ADD_KERNEL(mul);
         GGML_METAL_ADD_KERNEL(mul_row);
         GGML_METAL_ADD_KERNEL(scale);
+        GGML_METAL_ADD_KERNEL(scale_4);
         GGML_METAL_ADD_KERNEL(silu);
         GGML_METAL_ADD_KERNEL(relu);
         GGML_METAL_ADD_KERNEL(gelu);
@@ -254,6 +262,8 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
         GGML_METAL_ADD_KERNEL(get_rows_f16);
         GGML_METAL_ADD_KERNEL(get_rows_q4_0);
         GGML_METAL_ADD_KERNEL(get_rows_q4_1);
+        GGML_METAL_ADD_KERNEL(get_rows_q5_0);
+        GGML_METAL_ADD_KERNEL(get_rows_q5_1);
         GGML_METAL_ADD_KERNEL(get_rows_q8_0);
         GGML_METAL_ADD_KERNEL(get_rows_q2_K);
         GGML_METAL_ADD_KERNEL(get_rows_q3_K);
@@ -268,6 +278,8 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
         GGML_METAL_ADD_KERNEL(mul_mv_f16_f32_l4);
         GGML_METAL_ADD_KERNEL(mul_mv_q4_0_f32);
         GGML_METAL_ADD_KERNEL(mul_mv_q4_1_f32);
+        GGML_METAL_ADD_KERNEL(mul_mv_q5_0_f32);
+        GGML_METAL_ADD_KERNEL(mul_mv_q5_1_f32);
         GGML_METAL_ADD_KERNEL(mul_mv_q8_0_f32);
         GGML_METAL_ADD_KERNEL(mul_mv_q2_K_f32);
         GGML_METAL_ADD_KERNEL(mul_mv_q3_K_f32);
@@ -278,8 +290,10 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
             GGML_METAL_ADD_KERNEL(mul_mm_f32_f32);
             GGML_METAL_ADD_KERNEL(mul_mm_f16_f32);
             GGML_METAL_ADD_KERNEL(mul_mm_q4_0_f32);
-            GGML_METAL_ADD_KERNEL(mul_mm_q8_0_f32);
             GGML_METAL_ADD_KERNEL(mul_mm_q4_1_f32);
+            GGML_METAL_ADD_KERNEL(mul_mm_q5_0_f32);
+            GGML_METAL_ADD_KERNEL(mul_mm_q5_1_f32);
+            GGML_METAL_ADD_KERNEL(mul_mm_q8_0_f32);
             GGML_METAL_ADD_KERNEL(mul_mm_q2_K_f32);
             GGML_METAL_ADD_KERNEL(mul_mm_q3_K_f32);
             GGML_METAL_ADD_KERNEL(mul_mm_q4_K_f32);
@@ -335,6 +349,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
     GGML_METAL_DEL_KERNEL(mul);
     GGML_METAL_DEL_KERNEL(mul_row);
     GGML_METAL_DEL_KERNEL(scale);
+    GGML_METAL_DEL_KERNEL(scale_4);
     GGML_METAL_DEL_KERNEL(silu);
     GGML_METAL_DEL_KERNEL(relu);
     GGML_METAL_DEL_KERNEL(gelu);
@@ -346,6 +361,8 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
     GGML_METAL_DEL_KERNEL(get_rows_f16);
     GGML_METAL_DEL_KERNEL(get_rows_q4_0);
     GGML_METAL_DEL_KERNEL(get_rows_q4_1);
+    GGML_METAL_DEL_KERNEL(get_rows_q5_0);
+    GGML_METAL_DEL_KERNEL(get_rows_q5_1);
     GGML_METAL_DEL_KERNEL(get_rows_q8_0);
     GGML_METAL_DEL_KERNEL(get_rows_q2_K);
     GGML_METAL_DEL_KERNEL(get_rows_q3_K);
@@ -360,6 +377,8 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
     GGML_METAL_DEL_KERNEL(mul_mv_f16_f32_l4);
     GGML_METAL_DEL_KERNEL(mul_mv_q4_0_f32);
     GGML_METAL_DEL_KERNEL(mul_mv_q4_1_f32);
+    GGML_METAL_DEL_KERNEL(mul_mv_q5_0_f32);
+    GGML_METAL_DEL_KERNEL(mul_mv_q5_1_f32);
     GGML_METAL_DEL_KERNEL(mul_mv_q8_0_f32);
     GGML_METAL_DEL_KERNEL(mul_mv_q2_K_f32);
     GGML_METAL_DEL_KERNEL(mul_mv_q3_K_f32);
@@ -370,8 +389,10 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
         GGML_METAL_DEL_KERNEL(mul_mm_f32_f32);
         GGML_METAL_DEL_KERNEL(mul_mm_f16_f32);
         GGML_METAL_DEL_KERNEL(mul_mm_q4_0_f32);
-        GGML_METAL_DEL_KERNEL(mul_mm_q8_0_f32);
         GGML_METAL_DEL_KERNEL(mul_mm_q4_1_f32);
+        GGML_METAL_DEL_KERNEL(mul_mm_q5_0_f32);
+        GGML_METAL_DEL_KERNEL(mul_mm_q5_1_f32);
+        GGML_METAL_DEL_KERNEL(mul_mm_q8_0_f32);
         GGML_METAL_DEL_KERNEL(mul_mm_q2_K_f32);
         GGML_METAL_DEL_KERNEL(mul_mm_q3_K_f32);
         GGML_METAL_DEL_KERNEL(mul_mm_q4_K_f32);
@@ -905,15 +926,20 @@ void ggml_metal_graph_compute(
 
                             const float scale = *(const float *) src1->data;
 
-                            [encoder setComputePipelineState:ctx->pipeline_scale];
+                            int64_t n = ggml_nelements(dst);
+
+                            if (n % 4 == 0) {
+                                n /= 4;
+                                [encoder setComputePipelineState:ctx->pipeline_scale_4];
+                            } else {
+                                [encoder setComputePipelineState:ctx->pipeline_scale];
+                            }
+
                             [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                             [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
                             [encoder setBytes:&scale length:sizeof(scale) atIndex:2];
 
-                            const int64_t n = ggml_nelements(dst);
+                            [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
-                            GGML_ASSERT(n % 4 == 0);
-
-                            [encoder dispatchThreadgroups:MTLSizeMake(n/4, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                         } break;
                     case GGML_OP_UNARY:
                         switch (ggml_get_unary_op(gf->nodes[i])) {
@@ -1052,6 +1078,8 @@ void ggml_metal_graph_compute(
                                     case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_mul_mm_f16_f32];  break;
                                     case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_0_f32]; break;
                                     case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_1_f32]; break;
+                                    case GGML_TYPE_Q5_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_0_f32]; break;
+                                    case GGML_TYPE_Q5_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_1_f32]; break;
                                     case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q8_0_f32]; break;
                                     case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q2_K_f32]; break;
                                     case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q3_K_f32]; break;
@@ -1121,6 +1149,24 @@ void ggml_metal_graph_compute(
                                             nth1 = 8;
                                             [encoder setComputePipelineState:ctx->pipeline_mul_mv_q4_1_f32];
                                         } break;
+                                    case GGML_TYPE_Q5_0:
+                                        {
+                                            GGML_ASSERT(ne02 == 1);
+                                            GGML_ASSERT(ne12 == 1);
+
+                                            nth0 = 8;
+                                            nth1 = 8;
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_0_f32];
+                                        } break;
+                                    case GGML_TYPE_Q5_1:
+                                        {
+                                            GGML_ASSERT(ne02 == 1);
+                                            GGML_ASSERT(ne12 == 1);
+
+                                            nth0 = 8;
+                                            nth1 = 8;
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mv_q5_1_f32];
+                                        } break;
                                     case GGML_TYPE_Q8_0:
                                         {
                                             GGML_ASSERT(ne02 == 1);
@@ -1201,7 +1247,8 @@ void ggml_metal_graph_compute(
                                 [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:16];
                                 [encoder setBytes:&gqa  length:sizeof(gqa)  atIndex:17];
 
-                                if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q8_0 ||
+                                if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 ||
+                                    src0t == GGML_TYPE_Q5_0 || src0t == GGML_TYPE_Q5_1 || src0t == GGML_TYPE_Q8_0 ||
                                     src0t == GGML_TYPE_Q2_K) { // || src0t == GGML_TYPE_Q4_K) {
                                     [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                                 }
@@ -1233,6 +1280,8 @@ void ggml_metal_graph_compute(
                                 case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_get_rows_f16];  break;
                                 case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
                                 case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_1]; break;
+                                case GGML_TYPE_Q5_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_0]; break;
+                                case GGML_TYPE_Q5_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_1]; break;
                                 case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q8_0]; break;
                                 case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_K]; break;
                                 case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q3_K]; break;
@@ -1299,7 +1348,7 @@ void ggml_metal_graph_compute(
 
                             const int nth = MIN(1024, ne00);
 
-                            const int n_past = ((int32_t *) dst->op_params)[0]; UNUSED(n_past);
+                            //const int n_past = ((int32_t *) dst->op_params)[0];
                             const int n_head = ((int32_t *) dst->op_params)[1];
                             float max_bias;
                             memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));

ggml-metal.metal

@@ -18,6 +18,21 @@ typedef struct {
     uint8_t qs[QK4_1 / 2];  // nibbles / quants
 } block_q4_1;
 
+#define QK5_0 32
+typedef struct {
+    half d;                // delta
+    uint8_t qh[4];         // 5-th bit of quants
+    uint8_t qs[QK5_0 / 2]; // nibbles / quants
+} block_q5_0;
+
+#define QK5_1 32
+typedef struct {
+    half d;                 // delta
+    half m;                 // min
+    uint8_t qh[4];          // 5-th bit of quants
+    uint8_t qs[QK5_1 / 2];  // nibbles / quants
+} block_q5_1;
+
 #define QK8_0 32
 typedef struct {
     half    d;         // delta
@@ -110,9 +125,17 @@ kernel void kernel_mul_row(
 }
 
 kernel void kernel_scale(
+        device const float * src0,
+        device       float * dst,
+        constant     float & scale,
+        uint tpig[[thread_position_in_grid]]) {
+    dst[tpig] = src0[tpig] * scale;
+}
+
+kernel void kernel_scale_4(
         device const float4 * src0,
         device       float4 * dst,
-        constant     float & scale,
+        constant     float  & scale,
         uint tpig[[thread_position_in_grid]]) {
     dst[tpig] = src0[tpig] * scale;
 }
@@ -399,8 +422,11 @@ kernel void kernel_rms_norm(
 // that corresponds to the missing bit shifts (1, 1/16, 1/256, 1/4096)
 inline float block_q_n_dot_y(device const block_q4_0 * qb_curr, float sumy, thread float * yl, int il) {
     float d = qb_curr->d;
+
     float2 acc = 0.f;
+
     device const uint16_t * qs = ((device const uint16_t *)qb_curr + 1 + il/2);
+
     for (int i = 0; i < 8; i+=2) {
         acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F)
                 + yl[i + 1] * (qs[i / 2] & 0x0F00);
@@ -417,8 +443,11 @@ inline float block_q_n_dot_y(device const block_q4_0 * qb_curr, float sumy, thre
 inline float block_q_n_dot_y(device const block_q4_1 * qb_curr, float sumy, thread float * yl, int il) {
     float d = qb_curr->d;
     float m = qb_curr->m;
-    device const uint16_t * qs = ((device const uint16_t *)qb_curr + 2 + il/2);
+
     float2 acc = 0.f;
+
+    device const uint16_t * qs = ((device const uint16_t *)qb_curr + 2 + il/2);
+
     for (int i = 0; i < 8; i+=2) {
         acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F)
                 + yl[i + 1] * (qs[i / 2] & 0x0F00);
@@ -428,6 +457,49 @@ inline float block_q_n_dot_y(device const block_q4_1 * qb_curr, float sumy, thre
     return d * (acc[0] + acc[1]) + sumy * m;
 }
 
+// function for calculate inner product between half a q5_0 block and 16 floats (yl), sumy is SUM(yl[i])
+// il indicates where the q5 quants begin (0 or QK5_0/4)
+// we assume that the yl's have been multiplied with the appropriate scale factor
+// that corresponds to the missing bit shifts (1, 1/16, 1/256, 1/4096)
+inline float block_q_n_dot_y(device const block_q5_0 * qb_curr, float sumy, thread float * yl, int il) {
+    float d = qb_curr->d;
+
+    float2 acc = 0.f;
+
+    device const uint16_t * qs =  ((device const uint16_t *)qb_curr + 3 + il/2);
+           const uint32_t   qh = *((device const uint32_t *)qb_curr->qh);
+
+    for (int i = 0; i < 8; i+=2) {
+        acc[0] += yl[i + 0] * ((qs[i / 2] & 0x000F) | ((qh >> (i+0+il        ) << 4 ) & 0x00010))
+                + yl[i + 1] * ((qs[i / 2] & 0x0F00) | ((qh >> (i+1+il        ) << 12) & 0x01000));
+        acc[1] += yl[i + 8] * ((qs[i / 2] & 0x00F0) | ((qh >> (i+0+il+QK5_0/2) << 8 ) & 0x00100))
+                + yl[i + 9] * ((qs[i / 2] & 0xF000) | ((qh >> (i+1+il+QK5_0/2) << 16) & 0x10000));
+    }
+    return d * (sumy * -16.f + acc[0] + acc[1]);
+}
+
+// function for calculate inner product between half a q5_1 block and 16 floats (yl), sumy is SUM(yl[i])
+// il indicates where the q5 quants begin (0 or QK5_1/4)
+// we assume that the yl's have been multiplied with the appropriate scale factor
+// that corresponds to the missing bit shifts (1, 1/16, 1/256, 1/4096)
+inline float block_q_n_dot_y(device const block_q5_1 * qb_curr, float sumy, thread float * yl, int il) {
+    float d = qb_curr->d;
+    float m = qb_curr->m;
+
+    float2 acc = 0.f;
+
+    device const uint16_t * qs =  ((device const uint16_t *)qb_curr + 4 + il/2);
+           const uint32_t   qh = *((device const uint32_t *)qb_curr->qh);
+
+    for (int i = 0; i < 8; i+=2) {
+        acc[0] += yl[i + 0] * ((qs[i / 2] & 0x000F) | ((qh >> (i+0+il        ) << 4 ) & 0x00010))
+                + yl[i + 1] * ((qs[i / 2] & 0x0F00) | ((qh >> (i+1+il        ) << 12) & 0x01000));
+        acc[1] += yl[i + 8] * ((qs[i / 2] & 0x00F0) | ((qh >> (i+0+il+QK5_0/2) << 8 ) & 0x00100))
+                + yl[i + 9] * ((qs[i / 2] & 0xF000) | ((qh >> (i+1+il+QK5_0/2) << 16) & 0x10000));
+    }
+    return d * (acc[0] + acc[1]) + sumy * m;
+}
+
 // putting them in the kernel cause a significant performance penalty
 #define N_DST 4        // each SIMD group works on 4 rows
 #define N_SIMDGROUP 2  // number of SIMD groups in a thread group
@@ -525,6 +597,43 @@ kernel void kernel_mul_mv_q4_1_f32(
      mul_vec_q_n_f32<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,gqa,tgpig,tiisg,sgitg);
 }
 
+kernel void kernel_mul_mv_q5_0_f32(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01[[buffer(4)]],
+        constant   int64_t & ne02[[buffer(5)]],
+        constant   int64_t & ne10[[buffer(9)]],
+        constant   int64_t & ne12[[buffer(11)]],
+        constant   int64_t & ne0[[buffer(15)]],
+        constant   int64_t & ne1[[buffer(16)]],
+        constant   uint    & gqa[[buffer(17)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+    mul_vec_q_n_f32<block_q5_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,gqa,tgpig,tiisg,sgitg);
+}
+
+kernel void kernel_mul_mv_q5_1_f32(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01[[buffer(4)]],
+        constant   int64_t & ne02[[buffer(5)]],
+        constant   int64_t & ne10[[buffer(9)]],
+        constant   int64_t & ne12[[buffer(11)]],
+        constant   int64_t & ne0[[buffer(15)]],
+        constant   int64_t & ne1[[buffer(16)]],
+        constant   uint    & gqa[[buffer(17)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint  tiisg[[thread_index_in_simdgroup]],
+        uint  sgitg[[simdgroup_index_in_threadgroup]]) {
+    mul_vec_q_n_f32<block_q5_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,gqa,tgpig,tiisg,sgitg);
+}
+
+
 #define NB_Q8_0 8
 
 kernel void kernel_mul_mv_q8_0_f32(
@@ -2149,6 +2258,62 @@ void dequantize_q4_1(device const block_q4_1 *xb, short il, thread type4x4 & reg
     }
 }
 
+template <typename type4x4>
+void dequantize_q5_0(device const block_q5_0 *xb, short il, thread type4x4 & reg) {
+    device const uint16_t * qs = ((device const uint16_t *)xb + 3);
+    const float d = xb->d;
+    const float md = -16.h * xb->d;
+    const ushort mask = il ? 0x00F0 : 0x000F;
+
+    const uint32_t qh = *((device const uint32_t *)xb->qh);
+
+    const int x_mv = il ? 4 : 0;
+
+    const int gh_mv = il ? 12 : 0;
+    const int gh_bk = il ?  0 : 4;
+
+    for (int i = 0; i < 8; i++) {
+        // extract the 5-th bits for x0 and x1
+        const uint8_t xh_0 = ((qh >> (gh_mv + 2*i  )) << gh_bk) & 0x10;
+        const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10;
+
+        // combine the 4-bits from qs with the 5th bit
+        const int32_t x0 = ((((qs[i]     ) & mask) >> x_mv) | xh_0);
+        const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1);
+
+        reg[i/2][2*(i%2)+0] = d * x0 + md;
+        reg[i/2][2*(i%2)+1] = d * x1 + md;
+    }
+}
+
+template <typename type4x4>
+void dequantize_q5_1(device const block_q5_1 *xb, short il, thread type4x4 & reg) {
+    device const uint16_t * qs = ((device const uint16_t *)xb + 4);
+    const float d = xb->d;
+    const float m = xb->m;
+    const ushort mask = il ? 0x00F0 : 0x000F;
+
+    const uint32_t qh = *((device const uint32_t *)xb->qh);
+
+    const int x_mv = il ? 4 : 0;
+
+    const int gh_mv = il ? 12 : 0;
+    const int gh_bk = il ?  0 : 4;
+
+    for (int i = 0; i < 8; i++) {
+        // extract the 5-th bits for x0 and x1
+        const uint8_t xh_0 = ((qh >> (gh_mv + 2*i  )) << gh_bk) & 0x10;
+        const uint8_t xh_1 = ((qh >> (gh_mv + 2*i+1)) << gh_bk) & 0x10;
+
+        // combine the 4-bits from qs with the 5th bit
+        const int32_t x0 = ((((qs[i]     ) & mask) >> x_mv) | xh_0);
+        const int32_t x1 = ((((qs[i] >> 8) & mask) >> x_mv) | xh_1);
+
+        reg[i/2][2*(i%2)+0] = d * x0 + m;
+        reg[i/2][2*(i%2)+1] = d * x1 + m;
+    }
+}
+
 template <typename type4x4>
 void dequantize_q8_0(device const block_q8_0 *xb, short il, thread type4x4 & reg) {
     device const int8_t * qs = ((device const int8_t *)xb->qs);
@@ -2490,6 +2655,8 @@ template [[host_name("kernel_get_rows_f32")]]  kernel get_rows_t kernel_get_rows
 template [[host_name("kernel_get_rows_f16")]]  kernel get_rows_t kernel_get_rows<half4x4,    1, dequantize_f16>;
 template [[host_name("kernel_get_rows_q4_0")]] kernel get_rows_t kernel_get_rows<block_q4_0, 2, dequantize_q4_0>;
 template [[host_name("kernel_get_rows_q4_1")]] kernel get_rows_t kernel_get_rows<block_q4_1, 2, dequantize_q4_1>;
+template [[host_name("kernel_get_rows_q5_0")]] kernel get_rows_t kernel_get_rows<block_q5_0, 2, dequantize_q5_0>;
+template [[host_name("kernel_get_rows_q5_1")]] kernel get_rows_t kernel_get_rows<block_q5_1, 2, dequantize_q5_1>;
 template [[host_name("kernel_get_rows_q8_0")]] kernel get_rows_t kernel_get_rows<block_q8_0, 2, dequantize_q8_0>;
 template [[host_name("kernel_get_rows_q2_K")]] kernel get_rows_t kernel_get_rows<block_q2_K, QK_NL, dequantize_q2_K>;
 template [[host_name("kernel_get_rows_q3_K")]] kernel get_rows_t kernel_get_rows<block_q3_K, QK_NL, dequantize_q3_K>;
@@ -2518,6 +2685,8 @@ template [[host_name("kernel_mul_mm_f32_f32")]]  kernel mat_mm_t kernel_mul_mm<f
 template [[host_name("kernel_mul_mm_f16_f32")]]  kernel mat_mm_t kernel_mul_mm<half4x4,    1,     dequantize_f16>;
 template [[host_name("kernel_mul_mm_q4_0_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_0, 2,     dequantize_q4_0>;
 template [[host_name("kernel_mul_mm_q4_1_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_1, 2,     dequantize_q4_1>;
+template [[host_name("kernel_mul_mm_q5_0_f32")]] kernel mat_mm_t kernel_mul_mm<block_q5_0, 2,     dequantize_q5_0>;
+template [[host_name("kernel_mul_mm_q5_1_f32")]] kernel mat_mm_t kernel_mul_mm<block_q5_1, 2,     dequantize_q5_1>;
 template [[host_name("kernel_mul_mm_q8_0_f32")]] kernel mat_mm_t kernel_mul_mm<block_q8_0, 2,     dequantize_q8_0>;
 template [[host_name("kernel_mul_mm_q2_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q2_K, QK_NL, dequantize_q2_K>;
 template [[host_name("kernel_mul_mm_q3_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q3_K, QK_NL, dequantize_q3_K>;

ggml-opencl.cpp

@@ -19,7 +19,7 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
-#define CL_DMMV_BLOCK_SIZE 32
+#define CL_DMMV_LOCAL_SIZE 32
 
 #ifndef K_QUANTS_PER_ITERATION
 #define K_QUANTS_PER_ITERATION 1
@@ -338,7 +338,7 @@ __kernel void dequantize_mul_mat_vec_q2_K(__global const struct block_q2_K * xx,
     const int row = get_group_id(0);
 
     const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
+    const int ib0 = row*num_blocks_per_row + get_global_offset(0);
 
     __global const struct block_q2_K * x = xx + ib0;
 
@@ -413,7 +413,7 @@ __kernel void dequantize_mul_mat_vec_q3_K(__global const struct block_q3_K * xx,
     const int row = get_group_id(0);
 
     const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
+    const int ib0 = row*num_blocks_per_row + get_global_offset(0);
 
     __global const struct block_q3_K * x = xx + ib0;
 
@@ -489,7 +489,7 @@ __kernel void dequantize_mul_mat_vec_q4_K(__global const struct block_q4_K * xx,
 
     const int row = get_group_id(0);
     const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
+    const int ib0 = row*num_blocks_per_row + get_global_offset(0);
 
     const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION;  // 0...15
     const int ix  = get_local_id(0)%K_QUANTS_PER_ITERATION;
@@ -562,7 +562,7 @@ __kernel void dequantize_mul_mat_vec_q5_K(__global const struct block_q5_K * xx,
 
     const int row = get_group_id(0);
     const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
+    const int ib0 = row*num_blocks_per_row + get_global_offset(0);
 
     const int tid = get_local_id(0)/2;  // 0...15
     const int ix  = get_local_id(0)%2;
@@ -641,7 +641,7 @@ __kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx,
     const int row = get_group_id(0);
 
     const int num_blocks_per_row = ncols / QK_K;
-    const int ib0 = row*num_blocks_per_row;
+    const int ib0 = row*num_blocks_per_row + get_global_offset(0);
 
     __global const struct block_q6_K * x = xx + ib0;
 
@@ -745,19 +745,21 @@ __kernel void KERNEL_NAME(__global X_TYPE* x, __global float* y) {
 
 std::string dequant_mul_mat_vec_template = MULTILINE_QUOTE(
 __kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float* y, __global float* dst, const int ncols) {
-    const int block_size = get_local_size(0);
+    const int local_size = get_local_size(0);
     const int row = get_group_id(0);
     const int tid = get_local_id(0);
 
     const uint qk = QUANT_K;
     const uint qr = QUANT_R;
 
+    const int col_step = local_size * 2;
     const int y_offset = qr == 1 ? 1 : qk/2;
 
+    x += get_global_offset(0);
+
     tmp[tid] = 0;
 
-    for (int i = 0; i < ncols/block_size; i += 2) {
+    for (int col = tid*2; col < ncols; col += col_step) {
-        const int col = i*block_size + 2*tid;
         const int ib = (row*ncols + col)/qk; // block index
         const int iqs = (col%qk)/qr; // quant index
         const int iybs = col - col%qk; // y block start index
@@ -773,7 +775,7 @@ __kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float
 
     // sum up partial sums and write back result
     barrier(CLK_LOCAL_MEM_FENCE);
-    for (int s=block_size/2; s>0; s>>=1) {
+    for (int s=local_size/2; s>0; s>>=1) {
         if (tid < s) {
             tmp[tid] += tmp[tid + s];
         }
@@ -1393,75 +1395,46 @@ static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1,
     const int64_t ne01 = src0->ne[1];
     const int64_t ne02 = src0->ne[2];
     const int64_t ne03 = src0->ne[3];
-    const int64_t ne0 = ne00 * ne01 * ne02 * ne03;
     const int64_t ne10 = src1->ne[0];
     const int64_t ne11 = src1->ne[1];
     const int64_t ne12 = src1->ne[2];
     const int64_t ne13 = src1->ne[3];
-    const int64_t nb10 = src1->nb[0];
     const int nb2  = dst->nb[2];
     const int nb3  = dst->nb[3];
     size_t x_size;
     size_t d_size;
 
-    cl_mem d_X = ggml_cl_pool_malloc(ne0 * sizeof(float), &x_size); // src0
+    cl_mem d_X = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &x_size); // src0
     cl_mem d_Y = (cl_mem) src1->extra; // src1 is already on device, broadcasted.
-    cl_mem d_D = ggml_cl_pool_malloc(ne0 * sizeof(float), &d_size); // dst
+    cl_mem d_D = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &d_size); // dst
 
 
     for (int64_t i03 = 0; i03 < ne03; i03++) {
         for (int64_t i02 = 0; i02 < ne02; i02++) {
-            const int i0 = i03*ne02 + i02;
-
             cl_event ev;
 
             // copy src0 to device
-            CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, i0, src0, i03, i02, &ev));
+            CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, &ev));
 
-            if (nb10 == sizeof(float)) {
+            const int64_t i13 = i03%ne13;
-                // Contiguous, avoid overhead from queueing many kernel runs
+            const int64_t i12 = i02%ne12;
-                const int64_t i13 = i03%ne13;
+            const int i1 = i13*ne12*ne11 + i12*ne11;
-                const int64_t i12 = i02%ne12;
-                const int i1 = i13*ne12*ne11 + i12*ne11;
 
-                cl_int x_offset = 0;
+            cl_int x_offset = 0;
-                cl_int y_offset = i1*ne10;
+            cl_int y_offset = i1*ne10;
-                cl_int d_offset = 0;
+            cl_int d_offset = 0;
 
-                size_t global = ne00 * ne01;
+            size_t global = ne00 * ne01;
-                cl_int ky = ne10;
+            cl_int ky = ne10 * ne11;
-                CL_CHECK(clSetKernelArg(mul_f32_cl, 0, sizeof(cl_mem), &d_X));
-                CL_CHECK(clSetKernelArg(mul_f32_cl, 1, sizeof(cl_int), &x_offset));
-                CL_CHECK(clSetKernelArg(mul_f32_cl, 2, sizeof(cl_mem), &d_Y));
-                CL_CHECK(clSetKernelArg(mul_f32_cl, 3, sizeof(cl_int), &y_offset));
-                CL_CHECK(clSetKernelArg(mul_f32_cl, 4, sizeof(cl_mem), &d_D));
-                CL_CHECK(clSetKernelArg(mul_f32_cl, 5, sizeof(cl_int), &d_offset));
-                CL_CHECK(clSetKernelArg(mul_f32_cl, 6, sizeof(cl_int), &ky));
-                CL_CHECK(clEnqueueNDRangeKernel(queue, mul_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL));
-            } else {
-                for (int64_t i01 = 0; i01 < ne01; i01++) {
-                    const int64_t i13 = i03%ne13;
-                    const int64_t i12 = i02%ne12;
-                    const int64_t i11 = i01%ne11;
-                    const int i1 = i13*ne12*ne11 + i12*ne11 + i11;
 
-                    cl_int x_offset = i01*ne00;
+            CL_CHECK(clSetKernelArg(mul_f32_cl, 0, sizeof(cl_mem), &d_X));
-                    cl_int y_offset = i1*ne10;
+            CL_CHECK(clSetKernelArg(mul_f32_cl, 1, sizeof(cl_int), &x_offset));
-                    cl_int d_offset = i01*ne00;
+            CL_CHECK(clSetKernelArg(mul_f32_cl, 2, sizeof(cl_mem), &d_Y));
-
+            CL_CHECK(clSetKernelArg(mul_f32_cl, 3, sizeof(cl_int), &y_offset));
-                    // compute
+            CL_CHECK(clSetKernelArg(mul_f32_cl, 4, sizeof(cl_mem), &d_D));
-                    size_t global = ne00;
+            CL_CHECK(clSetKernelArg(mul_f32_cl, 5, sizeof(cl_int), &d_offset));
-                    cl_int ky = ne10;
+            CL_CHECK(clSetKernelArg(mul_f32_cl, 6, sizeof(cl_int), &ky));
-                    CL_CHECK(clSetKernelArg(mul_f32_cl, 0, sizeof(cl_mem), &d_X));
+            CL_CHECK(clEnqueueNDRangeKernel(queue, mul_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL));
-                    CL_CHECK(clSetKernelArg(mul_f32_cl, 1, sizeof(cl_int), &x_offset));
-                    CL_CHECK(clSetKernelArg(mul_f32_cl, 2, sizeof(cl_mem), &d_Y));
-                    CL_CHECK(clSetKernelArg(mul_f32_cl, 3, sizeof(cl_int), &y_offset));
-                    CL_CHECK(clSetKernelArg(mul_f32_cl, 4, sizeof(cl_mem), &d_D));
-                    CL_CHECK(clSetKernelArg(mul_f32_cl, 5, sizeof(cl_int), &d_offset));
-                    CL_CHECK(clSetKernelArg(mul_f32_cl, 6, sizeof(cl_int), &ky));
-                    CL_CHECK(clEnqueueNDRangeKernel(queue, mul_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL));
-                }
-            }
 
             CL_CHECK(clReleaseEvent(ev));
             CL_CHECK(clFinish(queue));
@@ -1516,46 +1489,45 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
     cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size);
 
     size_t x_offset = 0;
-    int64_t pi02 = -1;
-    int64_t pi03 = -1;
 
-    for (int64_t i13 = 0; i13 < ne13; i13++) {
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
-        int64_t i03 = i13 / r3;
+        // TODO: copy src0 here when r3>1
+        for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
+                if (src0->backend == GGML_BACKEND_GPU) {
+                    x_offset = (i03 * ne02 + i02) * x_ne;
+                } else {
+                    // copy src0 to device
+                    CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
+                }
 
-        for (int64_t i12 = 0; i12 < ne12; i12++) {
+                for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
-            int64_t i02 = i12 / r2;
+                    // copy src1 to device
+                    CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
 
-            // copy data to device
+                    CL_CHECK(clFinish(queue));
-            if (src0->backend == GGML_BACKEND_GPU) {
+
-                x_offset = (i03 * ne02 + i02) * x_ne;
+                    // compute
-            } else if (i02 != pi02 || i03 != pi03) {
+                    cl_event ev_sgemm;
-                CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
+                    clblast::StatusCode status = clblast::Gemm<cl_float>(clblast::Layout::kColMajor,
-                pi02 = i02;
+                                                               clblast::Transpose::kYes, clblast::Transpose::kNo,
-                pi03 = i03;
+                                                               ne01, ne11, ne10,
+                                                               alpha,
+                                                               d_X, x_offset, ne00,
+                                                               d_Y, 0, ne10,
+                                                               beta,
+                                                               d_D, 0, ne01,
+                                                               &queue, &ev_sgemm);
+
+                    if (status != clblast::StatusCode::kSuccess) {
+                        GGML_ASSERT(false);
+                    }
+
+                    // copy dst to host
+                    float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
+                    CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL));
+                }
             }
-            CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
-
-            CL_CHECK(clFinish(queue));
-
-            // compute
-            cl_event ev_sgemm;
-            clblast::StatusCode status = clblast::Gemm<cl_float>(clblast::Layout::kColMajor,
-                                                       clblast::Transpose::kYes, clblast::Transpose::kNo,
-                                                       ne01, ne11, ne10,
-                                                       alpha,
-                                                       d_X, x_offset, ne00,
-                                                       d_Y, 0, ne10,
-                                                       beta,
-                                                       d_D, 0, ne01,
-                                                       &queue, &ev_sgemm);
-
-            if (status != clblast::StatusCode::kSuccess) {
-                GGML_ASSERT(false);
-            }
-
-            // copy dst to host
-            float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
-            CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL));
         }
     }
 
@@ -1566,7 +1538,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr
     ggml_cl_pool_free(d_D, d_size);
 }
 
-static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t /* wsize */) {
+static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t wsize) {
     GGML_ASSERT(fp16_support);
 
     const int64_t ne00 = src0->ne[0];
@@ -1596,6 +1568,10 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
     const int y_ne = ne11 * ne10;
     const int d_ne = ne11 * ne01;
 
+    GGML_ASSERT(wsize >= sizeof(ggml_fp16_t) * y_ne);
+    GGML_ASSERT(wsize >= sizeof(ggml_fp16_t) * d_ne);
+    ggml_fp16_t * const tmp = (ggml_fp16_t *) wdata;
+
     size_t x_size;
     size_t y_size;
     size_t d_size;
@@ -1612,74 +1588,70 @@ static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * sr
     bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float);
 
     size_t x_offset = 0;
-    int64_t pi02 = -1;
-    int64_t pi03 = -1;
 
-    for (int64_t i13 = 0; i13 < ne13; i13++) {
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
-        int64_t i03 = i13 / r3;
+        // TODO: copy src0 here when r3>1
-
+        for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
-        for (int64_t i12 = 0; i12 < ne12; i12++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
-            int64_t i02 = i12 / r2;
+                if (src0->backend == GGML_BACKEND_GPU) {
-
+                    x_offset = (i03 * ne02 + i02) * x_ne;
-            // copy src0 to device
+                } else {
-            if (src0->backend == GGML_BACKEND_GPU) {
+                    // copy src0 to device
-                x_offset = (i03 * ne02 + i02) * x_ne;
+                    CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
-            } else if (i02 != pi02 || i03 != pi03) {
-                CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL));
-                pi02 = i02;
-                pi03 = i03;
-            }
-
-            // convert src1 to fp16
-            // TODO: use multiple threads
-            ggml_fp16_t * const tmp = (ggml_fp16_t *) wdata + (ne11 * ne10) * (i13 * ne12 + i12);
-            char * src1i = (char *) src1->data + i13*nb13 + i12*nb12;
-            if (src1_cont_rows) {
-                if (src1_cont_cols) {
-                    ggml_fp32_to_fp16_row((float *) src1i, tmp, ne10*ne11);
                 }
-                else {
+
-                    for (int64_t i11 = 0; i11 < ne11; i11++) {
+                for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
-                        ggml_fp32_to_fp16_row((float *) (src1i + i11*nb11), tmp + i11*ne10, ne10);
+                    // convert src1 to fp16
+                    // TODO: use multiple threads
+                    char * src1i = (char *) src1->data + i13*nb13 + i12*nb12;
+                    if (src1_cont_rows) {
+                        if (src1_cont_cols) {
+                            ggml_fp32_to_fp16_row((float *) src1i, tmp, ne10*ne11);
+                        }
+                        else {
+                            for (int64_t i11 = 0; i11 < ne11; i11++) {
+                                ggml_fp32_to_fp16_row((float *) (src1i + i11*nb11), tmp + i11*ne10, ne10);
+                            }
+                        }
                     }
-                }
+                    else {
-            }
+                        for (int64_t i11 = 0; i11 < ne11; i11++) {
-            else {
+                            for (int64_t i10 = 0; i10 < ne10; i10++) {
-                for (int64_t i11 = 0; i11 < ne11; i11++) {
+                                // very slow due to no inlining
-                    for (int64_t i10 = 0; i10 < ne10; i10++) {
+                                tmp[i11*ne10 + i10] = ggml_fp32_to_fp16(*(float *) (src1i + i11*nb11 + i10*nb10));
-                        // very slow due to no inlining
+                            }
-                        tmp[i11*ne10 + i10] = ggml_fp32_to_fp16(*(float *) (src1i + i11*nb11 + i10*nb10));
+                        }
                     }
+
+                    // copy src1 to device
+                    CL_CHECK(clEnqueueWriteBuffer(queue, d_Y, false, 0, sizeof(ggml_fp16_t) * y_ne, tmp, 0, NULL, NULL));
+
+                    CL_CHECK(clFinish(queue));
+
+                    // compute
+                    cl_event ev_sgemm;
+                    clblast::StatusCode status = clblast::Gemm<cl_half>(clblast::Layout::kColMajor,
+                                                               clblast::Transpose::kYes, clblast::Transpose::kNo,
+                                                               ne01, ne11, ne10,
+                                                               alpha,
+                                                               d_X, x_offset, ne00,
+                                                               d_Y, 0, ne10,
+                                                               beta,
+                                                               d_D, 0, ne01,
+                                                               &queue, &ev_sgemm);
+
+                    if (status != clblast::StatusCode::kSuccess) {
+                        GGML_ASSERT(false);
+                    }
+
+                    // copy dst to host, then convert to float
+                    CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL));
+
+                    float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
+
+                    ggml_fp16_to_fp32_row(tmp, d, d_ne);
                 }
             }
-
-            // copy src1 to device
-            CL_CHECK(clEnqueueWriteBuffer(queue, d_Y, false, 0, sizeof(ggml_fp16_t) * y_ne, tmp, 0, NULL, NULL));
-
-            CL_CHECK(clFinish(queue));
-
-            // compute
-            cl_event ev_sgemm;
-            clblast::StatusCode status = clblast::Gemm<cl_half>(clblast::Layout::kColMajor,
-                                                       clblast::Transpose::kYes, clblast::Transpose::kNo,
-                                                       ne01, ne11, ne10,
-                                                       alpha,
-                                                       d_X, x_offset, ne00,
-                                                       d_Y, 0, ne10,
-                                                       beta,
-                                                       d_D, 0, ne01,
-                                                       &queue, &ev_sgemm);
-
-            if (status != clblast::StatusCode::kSuccess) {
-                GGML_ASSERT(false);
-            }
-
-            // copy dst to host, then convert to float
-            CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL));
-
-            float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
-
-            ggml_fp16_to_fp32_row(tmp, d, d_ne);
         }
     }
 
@@ -1704,7 +1676,7 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
     const int nb2  = dst->nb[2];
     const int nb3  = dst->nb[3];
     const ggml_type type = src0->type;
-    const bool mul_mat_vec = ne11 == 1;
+    const bool mul_mat_vec = ne11 == 1 && ne00%2 == 0;
 
     const int64_t r2 = ne12 / ne02;
     const int64_t r3 = ne13 / ne03;
@@ -1737,90 +1709,86 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
     GGML_ASSERT(to_fp32_cl != nullptr);
 
     const size_t global_denom = ggml_cl_global_denom(type);
-    const size_t local = ggml_cl_local_size(type);
+    const size_t local = mul_mat_vec ? CL_DMMV_LOCAL_SIZE : ggml_cl_local_size(type);
 
     size_t ev_idx = 0;
     std::vector<cl_event> events;
 
-    int64_t pi02 = -1;
+    for (int64_t i03 = 0; i03 < ne03; i03++) {
-    int64_t pi03 = -1;
+        // TODO: copy and dequantize src0 here when r3>1
-
+        for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) {
-    for (int64_t i13 = 0; i13 < ne13; i13++) {
+            for (int64_t i02 = 0; i02 < ne02; i02++) {
-        int64_t i03 = i13 / r3;
+                // copy src0 to device if necessary
-
+                if (src0->backend == GGML_BACKEND_CPU) {
-        for (int64_t i12 = 0; i12 < ne12; i12++) {
-            int64_t i02 = i12 / r2;
-
-            // copy src0 to device if necessary
-            if (src0->backend == GGML_BACKEND_CPU) {
-                if (i02 != pi02 || i03 != pi03) {
                     events.emplace_back();
                     CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++));
-                    pi02 = i02;
+                } else if (src0->backend == GGML_BACKEND_GPU) {
-                    pi03 = i03;
+                    d_Q = (cl_mem) src0->extra;
-                }
+                } else {
-            } else if (src0->backend == GGML_BACKEND_GPU) {
-                d_Q = (cl_mem) src0->extra;
-            } else {
-                GGML_ASSERT(false);
-            }
-            if (mul_mat_vec) { // specialized dequantize_mul_mat_vec kernel
-                // copy src1 to device
-                events.emplace_back();
-                CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, events.data() + ev_idx++));
-
-                // compute
-                const size_t global = ne01 * CL_DMMV_BLOCK_SIZE;
-                const size_t local = CL_DMMV_BLOCK_SIZE;
-                const cl_int ncols = ne00;
-                events.emplace_back();
-                CL_CHECK(clSetKernelArg(*dmmv, 0, sizeof(cl_mem), &d_Q));
-                CL_CHECK(clSetKernelArg(*dmmv, 1, sizeof(float) * local, NULL));
-                CL_CHECK(clSetKernelArg(*dmmv, 2, sizeof(cl_mem), &d_Y));
-                CL_CHECK(clSetKernelArg(*dmmv, 3, sizeof(cl_mem), &d_D));
-                CL_CHECK(clSetKernelArg(*dmmv, 4, sizeof(cl_int), &ncols));
-                CL_CHECK(clEnqueueNDRangeKernel(queue, *dmmv, 1, NULL, &global, &local, events.size() - 1, events.data(), events.data() + ev_idx++));
-            } else { // general dequantization kernel + CLBlast matrix matrix multiplication
-                // convert src0 to fp32 on device
-                const size_t global = x_ne / global_denom;
-                const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0;
-                CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q));
-                CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X));
-                CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, offset > 0 ? &offset : NULL, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
-
-                // copy src1 to device
-                CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
-
-                events.emplace_back();
-
-                // wait for conversion
-                CL_CHECK(clFinish(queue));
-
-                // compute
-                clblast::StatusCode status = clblast::Gemm<cl_float>(clblast::Layout::kColMajor,
-                                                           clblast::Transpose::kYes, clblast::Transpose::kNo,
-                                                           ne01, ne11, ne10,
-                                                           alpha,
-                                                           d_X, 0, ne00,
-                                                           d_Y, 0, ne10,
-                                                           beta,
-                                                           d_D, 0, ne01,
-                                                           &queue, events.data() + ev_idx++);
-
-                if (status != clblast::StatusCode::kSuccess) {
                     GGML_ASSERT(false);
                 }
-            }
 
-            // copy dst to host
+                if (!mul_mat_vec) {
-            float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
+                    // convert src0 to fp32 on device
-            CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &events[events.size() - 1], NULL));
+                    const size_t global = x_ne / global_denom;
-            for (auto *event : events) {
+                    const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0;
-                clReleaseEvent(event);
+                    CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q));
-            }
+                    CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X));
+                    CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, &offset, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
+                }
 
-            ev_idx = 0;
+                for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) {
-            events.clear();
+                    if (mul_mat_vec) { // specialized dequantize_mul_mat_vec kernel
+                        // copy src1 to device
+                        events.emplace_back();
+                        CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, events.data() + ev_idx++));
+
+                        // compute
+                        const size_t global = ne01 * local;
+                        const size_t offset = src0->backend == GGML_BACKEND_GPU ? (i03 * ne02 + i02) * x_bps : 0;
+                        const cl_int ncols = ne00;
+                        events.emplace_back();
+                        CL_CHECK(clSetKernelArg(*dmmv, 0, sizeof(cl_mem), &d_Q));
+                        CL_CHECK(clSetKernelArg(*dmmv, 1, sizeof(float) * local, NULL));
+                        CL_CHECK(clSetKernelArg(*dmmv, 2, sizeof(cl_mem), &d_Y));
+                        CL_CHECK(clSetKernelArg(*dmmv, 3, sizeof(cl_mem), &d_D));
+                        CL_CHECK(clSetKernelArg(*dmmv, 4, sizeof(cl_int), &ncols));
+                        CL_CHECK(clEnqueueNDRangeKernel(queue, *dmmv, 1, &offset, &global, &local, events.size() - 1, events.data(), events.data() + ev_idx++));
+                    } else { // CLBlast matrix matrix multiplication
+                        // copy src1 to device
+                        CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL));
+
+                        // wait for conversion
+                        CL_CHECK(clFinish(queue));
+
+                        // compute
+                        events.emplace_back();
+                        clblast::StatusCode status = clblast::Gemm<cl_float>(clblast::Layout::kColMajor,
+                                                                   clblast::Transpose::kYes, clblast::Transpose::kNo,
+                                                                   ne01, ne11, ne10,
+                                                                   alpha,
+                                                                   d_X, 0, ne00,
+                                                                   d_Y, 0, ne10,
+                                                                   beta,
+                                                                   d_D, 0, ne01,
+                                                                   &queue, events.data() + ev_idx++);
+
+                        if (status != clblast::StatusCode::kSuccess) {
+                            GGML_ASSERT(false);
+                        }
+                    }
+
+                    // copy dst to host
+                    float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3);
+                    CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &events[events.size() - 1], NULL));
+                    for (auto *event : events) {
+                        clReleaseEvent(event);
+                    }
+
+                    ev_idx = 0;
+                    events.clear();
+                }
+            }
         }
     }
 
@@ -1895,8 +1863,8 @@ void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor *
 }
 
 size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) {
-    if (ggml_cl_mul_mat_use_f16(src0, src1, dst)) {
+    if (src0->type == GGML_TYPE_F16 && ggml_cl_mul_mat_use_f16(src0, src1, dst)) {
-        return ggml_nelements(src1) * sizeof(ggml_fp16_t);
+        return sizeof(ggml_fp16_t) * std::max(src1->ne[0] * src1->ne[1], dst->ne[0] * dst->ne[1]);
     }
     return 0;
 }

ggml.c

@@ -5494,6 +5494,39 @@ struct ggml_tensor * ggml_view_tensor(
     return result;
 }
 
+struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx) {
+    struct ggml_object * obj = ctx->objects_begin;
+
+    char * const mem_buffer = ctx->mem_buffer;
+
+    while (obj != NULL) {
+        if (obj->type == GGML_OBJECT_TENSOR) {
+            return (struct ggml_tensor *)(mem_buffer + obj->offs);
+        }
+
+        obj = obj->next;
+    }
+
+    return NULL;
+}
+
+struct ggml_tensor * ggml_get_next_tensor(struct ggml_context * ctx, struct ggml_tensor * tensor) {
+    struct ggml_object * obj = (struct ggml_object *) ((char *)tensor - GGML_OBJECT_SIZE);
+    obj = obj->next;
+
+    char * const mem_buffer = ctx->mem_buffer;
+
+    while (obj != NULL) {
+        if (obj->type == GGML_OBJECT_TENSOR) {
+            return (struct ggml_tensor *)(mem_buffer + obj->offs);
+        }
+
+        obj = obj->next;
+    }
+
+    return NULL;
+}
+
 struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name) {
     struct ggml_object * obj = ctx->objects_begin;
 
@@ -8647,6 +8680,7 @@ void ggml_set_param(
 
     GGML_ASSERT(tensor->grad == NULL);
     tensor->grad = ggml_dup_tensor(ctx, tensor);
+    ggml_format_name(tensor->grad, "%s (grad)", tensor->name);
 }
 
 // ggml_compute_forward_dup
@@ -13059,13 +13093,11 @@ static void ggml_compute_forward_alibi_f32(
         return;
     }
 
-    const int n_past = ((int32_t *) dst->op_params)[0]; UNUSED(n_past);
+    //const int n_past = ((int32_t *) dst->op_params)[0];
     const int n_head = ((int32_t *) dst->op_params)[1];
     float max_bias;
     memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
 
-    assert(n_past >= 0);
-
     const int64_t ne0 = src0->ne[0]; // all_seq_len = n_past + ne1
     const int64_t ne1 = src0->ne[1]; // seq_len_without_past
     const int64_t ne2 = src0->ne[2]; // n_head -> this is k
@@ -13505,7 +13537,7 @@ static void ggml_compute_forward_rope_f16(
                         dst_data[n_dims]     = GGML_FP32_TO_FP16(x2*cos_block_theta - x3*sin_block_theta);
                         dst_data[n_dims/2*3] = GGML_FP32_TO_FP16(x2*sin_block_theta + x3*cos_block_theta);
                     }
-                } if (!is_neox) {
+                } else if (!is_neox) {
                     for (int64_t i0 = 0; i0 < ne0; i0 += 2) {
                         const float cos_theta = cosf(theta);
                         const float sin_theta = sinf(theta);
@@ -14430,7 +14462,7 @@ static void ggml_compute_forward_conv_2d_f16_f32(
     int64_t t0 = ggml_perf_time_us();
     UNUSED(t0);
 
-    GGML_TENSOR_BINARY_OP_LOCALS
+    GGML_TENSOR_BINARY_OP_LOCALS;
 
     const int ith = params->ith;
     const int nth = params->nth;
@@ -19138,6 +19170,7 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) {
 
                             if (idx == -1) {
                                 fprintf(stderr, "%s: failed to find tensor, arg = %d, node = %d\n", __func__, j, i);
+                                fclose(fout);
                                 return;
                             }
 
@@ -20812,7 +20845,7 @@ struct gguf_kv {
 };
 
 struct gguf_header {
-    uint32_t magic;
+    char magic[4];
     uint32_t version;
     uint64_t n_tensors; // GGUFv2
     uint64_t n_kv;      // GGUFv2
@@ -20882,7 +20915,7 @@ static bool gguf_fread_str_v1(FILE * file, struct gguf_str * p, size_t * offset)
 struct gguf_context * gguf_init_empty(void) {
     struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context));
 
-    ctx->header.magic     = GGUF_MAGIC;
+    memcpy(ctx->header.magic, GGUF_MAGIC, sizeof(ctx->header.magic));
     ctx->header.version   = GGUF_VERSION;
     ctx->header.n_tensors = 0;
     ctx->header.n_kv      = 0;
@@ -20908,16 +20941,18 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
     // offset from start of file
     size_t offset = 0;
 
-    uint32_t magic = 0;
+    char magic[4];
 
     // check the magic before making allocations
     {
         gguf_fread_el(file, &magic, sizeof(magic), &offset);
 
-        if (magic != GGUF_MAGIC) {
+        for (uint32_t i = 0; i < sizeof(magic); i++) {
-            fprintf(stderr, "%s: invalid magic number %08x\n", __func__, magic);
+            if (magic[i] != GGUF_MAGIC[i]) {
-            fclose(file);
+                fprintf(stderr, "%s: invalid magic characters %s.\n", __func__, magic);
-            return NULL;
+                fclose(file);
+                return NULL;
+            }
         }
     }
 
@@ -20927,7 +20962,8 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
 
     // read the header
     {
-        ctx->header.magic = magic;
+        strncpy(ctx->header.magic, magic, 4);
+
 
         ctx->kv    = NULL;
         ctx->infos = NULL;

ggml.h

@@ -231,8 +231,9 @@
 #define GGML_EXIT_SUCCESS 0
 #define GGML_EXIT_ABORTED 1
 
-#define GGUF_MAGIC   0x46554747 // "GGUF"
+#define GGUF_MAGIC "GGUF"
-#define GGUF_VERSION 2
+
+#define GGUF_VERSION 3
 
 #define GGUF_DEFAULT_ALIGNMENT 32
 
@@ -704,6 +705,9 @@ extern "C" {
     GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src);
     GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, struct ggml_tensor * src);
 
+    // Context tensor enumeration and lookup
+    GGML_API struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx);
+    GGML_API struct ggml_tensor * ggml_get_next_tensor (struct ggml_context * ctx, struct ggml_tensor * tensor);
     GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name);
 
     GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);

gguf-py/gguf/gguf.py

@@ -19,9 +19,10 @@ import numpy as np
 #
 
 GGUF_MAGIC             = 0x46554747
-GGUF_VERSION           = 2
+GGUF_VERSION           = 3
 GGUF_DEFAULT_ALIGNMENT = 32
 
+
 # general
 KEY_GENERAL_ARCHITECTURE         = "general.architecture"
 KEY_GENERAL_QUANTIZATION_VERSION = "general.quantization_version"
@@ -88,29 +89,31 @@ class MODEL_ARCH(IntEnum):
     PERSIMMON     : int = auto()
     REFACT        : int = auto()
     BERT          : int = auto()
+    BLOOM         : int = auto()
 
 
 class MODEL_TENSOR(IntEnum):
-    TOKEN_EMBD   : int = auto()
+    TOKEN_EMBD      : int = auto()
-    TOKEN_TYPES  : int = auto()
+    TOKEN_EMBD_NORM : int = auto()
-    POS_EMBD     : int = auto()
+    TOKEN_TYPES     : int = auto()
-    OUTPUT       : int = auto()
+    POS_EMBD        : int = auto()
-    OUTPUT_NORM  : int = auto()
+    OUTPUT          : int = auto()
-    ROPE_FREQS   : int = auto()
+    OUTPUT_NORM     : int = auto()
-    ATTN_Q       : int = auto()
+    ROPE_FREQS      : int = auto()
-    ATTN_K       : int = auto()
+    ATTN_Q          : int = auto()
-    ATTN_V       : int = auto()
+    ATTN_K          : int = auto()
-    ATTN_QKV     : int = auto()
+    ATTN_V          : int = auto()
-    ATTN_OUT     : int = auto()
+    ATTN_QKV        : int = auto()
-    ATTN_NORM    : int = auto()
+    ATTN_OUT        : int = auto()
-    ATTN_NORM_2  : int = auto()
+    ATTN_NORM       : int = auto()
-    ATTN_ROT_EMBD: int = auto()
+    ATTN_NORM_2     : int = auto()
-    FFN_GATE     : int = auto()
+    ATTN_ROT_EMBD   : int = auto()
-    FFN_DOWN     : int = auto()
+    FFN_GATE        : int = auto()
-    FFN_UP       : int = auto()
+    FFN_DOWN        : int = auto()
-    FFN_NORM     : int = auto()
+    FFN_UP          : int = auto()
-    ATTN_Q_NORM  : int = auto()
+    FFN_NORM        : int = auto()
-    ATTN_K_NORM  : int = auto()
+    ATTN_Q_NORM     : int = auto()
+    ATTN_K_NORM     : int = auto()
 
 
 MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
@@ -125,29 +128,31 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.PERSIMMON:      "persimmon",
     MODEL_ARCH.REFACT:         "refact",
     MODEL_ARCH.BERT:           "bert",
+    MODEL_ARCH.BLOOM:          "bloom",
 }
 
 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
-    MODEL_TENSOR.TOKEN_EMBD:    "token_embd",
+    MODEL_TENSOR.TOKEN_EMBD:      "token_embd",
-    MODEL_TENSOR.TOKEN_TYPES:   "token_types",
+    MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm",
-    MODEL_TENSOR.POS_EMBD:      "position_embd",
+    MODEL_TENSOR.TOKEN_TYPES:     "token_types",
-    MODEL_TENSOR.OUTPUT_NORM:   "output_norm",
+    MODEL_TENSOR.POS_EMBD:        "position_embd",
-    MODEL_TENSOR.OUTPUT:        "output",
+    MODEL_TENSOR.OUTPUT_NORM:     "output_norm",
-    MODEL_TENSOR.ROPE_FREQS:    "rope_freqs",
+    MODEL_TENSOR.OUTPUT:          "output",
-    MODEL_TENSOR.ATTN_NORM:     "blk.{bid}.attn_norm",
+    MODEL_TENSOR.ROPE_FREQS:      "rope_freqs",
-    MODEL_TENSOR.ATTN_NORM_2:   "blk.{bid}.attn_norm_2",
+    MODEL_TENSOR.ATTN_NORM:       "blk.{bid}.attn_norm",
-    MODEL_TENSOR.ATTN_QKV:      "blk.{bid}.attn_qkv",
+    MODEL_TENSOR.ATTN_NORM_2:     "blk.{bid}.attn_norm_2",
-    MODEL_TENSOR.ATTN_Q:        "blk.{bid}.attn_q",
+    MODEL_TENSOR.ATTN_QKV:        "blk.{bid}.attn_qkv",
-    MODEL_TENSOR.ATTN_K:        "blk.{bid}.attn_k",
+    MODEL_TENSOR.ATTN_Q:          "blk.{bid}.attn_q",
-    MODEL_TENSOR.ATTN_V:        "blk.{bid}.attn_v",
+    MODEL_TENSOR.ATTN_K:          "blk.{bid}.attn_k",
-    MODEL_TENSOR.ATTN_OUT:      "blk.{bid}.attn_output",
+    MODEL_TENSOR.ATTN_V:          "blk.{bid}.attn_v",
-    MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd",
+    MODEL_TENSOR.ATTN_OUT:        "blk.{bid}.attn_output",
-    MODEL_TENSOR.ATTN_Q_NORM:   "blk.{bid}.attn_q_norm",
+    MODEL_TENSOR.ATTN_ROT_EMBD:   "blk.{bid}.attn_rot_embd",
-    MODEL_TENSOR.ATTN_K_NORM:   "blk.{bid}.attn_k_norm",
+    MODEL_TENSOR.ATTN_Q_NORM:     "blk.{bid}.attn_q_norm",
-    MODEL_TENSOR.FFN_NORM:      "blk.{bid}.ffn_norm",
+    MODEL_TENSOR.ATTN_K_NORM:     "blk.{bid}.attn_k_norm",
-    MODEL_TENSOR.FFN_GATE:      "blk.{bid}.ffn_gate",
+    MODEL_TENSOR.FFN_NORM:        "blk.{bid}.ffn_norm",
-    MODEL_TENSOR.FFN_DOWN:      "blk.{bid}.ffn_down",
+    MODEL_TENSOR.FFN_GATE:        "blk.{bid}.ffn_gate",
-    MODEL_TENSOR.FFN_UP:        "blk.{bid}.ffn_up",
+    MODEL_TENSOR.FFN_DOWN:        "blk.{bid}.ffn_down",
+    MODEL_TENSOR.FFN_UP:          "blk.{bid}.ffn_up",
 }
 
 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -282,6 +287,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.BLOOM: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.TOKEN_EMBD_NORM,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
     MODEL_ARCH.GPT2: [
         # TODO
     ],
@@ -311,6 +328,7 @@ class TensorNameMap:
             "gpt_neox.embed_in",                        # gptneox
             "transformer.wte",                          # gpt2 gpt-j mpt refact
             "transformer.word_embeddings",              # falcon
+            "word_embeddings",                          # bloom
             "model.embed_tokens",                       # llama-hf
             "tok_embeddings",                           # llama-pth
             "embeddings.word_embeddings",               # bert
@@ -322,6 +340,11 @@ class TensorNameMap:
             "embeddings.token_type_embeddings",  # bert
         ),
 
+        # Normalization of token embeddings
+        MODEL_TENSOR.TOKEN_EMBD_NORM: (
+            "word_embeddings_layernorm",  # bloom
+        ),
+
         # Position embeddings
         MODEL_TENSOR.POS_EMBD: (
             "transformer.wpe",                 # gpt2
@@ -332,7 +355,7 @@ class TensorNameMap:
         MODEL_TENSOR.OUTPUT: (
             "embed_out",                # gptneox
             "lm_head",                  # gpt2 mpt falcon llama-hf baichuan
-            "output",                   # llama-pth
+            "output",                   # llama-pth bloom
             "word_embeddings_for_head", # persimmon
         ),
 
@@ -344,7 +367,7 @@ class TensorNameMap:
             "norm",                                   # llama-pth
             "embeddings.LayerNorm",                   # bert
             "transformer.norm_f",                     # mpt
-            "ln_f",                                   # refact
+            "ln_f",                                   # refact bloom
             "language_model.encoder.final_layernorm", # persimmon
         ),
 
@@ -361,6 +384,7 @@ class TensorNameMap:
             "transformer.h.{bid}.ln_1",                            # gpt2 gpt-j refact
             "transformer.blocks.{bid}.norm_1",                     # mpt
             "transformer.h.{bid}.input_layernorm",                 # falcon7b
+            "h.{bid}.input_layernorm",                             # bloom
             "transformer.h.{bid}.ln_mlp",                          # falcon40b
             "model.layers.{bid}.input_layernorm",                  # llama-hf
             "layers.{bid}.attention_norm",                         # llama-pth
@@ -379,6 +403,7 @@ class TensorNameMap:
             "transformer.h.{bid}.attn.c_attn",                                    # gpt2
             "transformer.blocks.{bid}.attn.Wqkv",                                 # mpt
             "transformer.h.{bid}.self_attention.query_key_value",                 # falcon
+            "h.{bid}.self_attention.query_key_value",                             # bloom
             "language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon
         ),
 
@@ -412,6 +437,7 @@ class TensorNameMap:
             "transformer.h.{bid}.attn.c_proj",                         # gpt2 refact
             "transformer.blocks.{bid}.attn.out_proj",                  # mpt
             "transformer.h.{bid}.self_attention.dense",                # falcon
+            "h.{bid}.self_attention.dense",                            # bloom
             "model.layers.{bid}.self_attn.o_proj",                     # llama-hf
             "layers.{bid}.attention.wo",                               # llama-pth
             "encoder.layer.{bid}.attention.output.dense",              # bert
@@ -429,6 +455,7 @@ class TensorNameMap:
         MODEL_TENSOR.FFN_NORM: (
             "gpt_neox.layers.{bid}.post_attention_layernorm",               # gptneox
             "transformer.h.{bid}.ln_2",                                     # gpt2 refact
+            "h.{bid}.post_attention_layernorm",                             # bloom
             "transformer.blocks.{bid}.norm_2",                              # mpt
             "model.layers.{bid}.post_attention_layernorm",                  # llama-hf
             "layers.{bid}.ffn_norm",                                        # llama-pth
@@ -442,6 +469,7 @@ class TensorNameMap:
             "transformer.h.{bid}.mlp.c_fc",                          # gpt2
             "transformer.blocks.{bid}.ffn.up_proj",                  # mpt
             "transformer.h.{bid}.mlp.dense_h_to_4h",                 # falcon
+            "h.{bid}.mlp.dense_h_to_4h",                             # bloom
             "model.layers.{bid}.mlp.up_proj",                        # llama-hf refact
             "layers.{bid}.feed_forward.w3",                          # llama-pth
             "encoder.layer.{bid}.intermediate.dense",                # bert
@@ -461,6 +489,7 @@ class TensorNameMap:
             "transformer.h.{bid}.mlp.c_proj",                        # gpt2 refact
             "transformer.blocks.{bid}.ffn.down_proj",                # mpt
             "transformer.h.{bid}.mlp.dense_4h_to_h",                 # falcon
+            "h.{bid}.mlp.dense_4h_to_h",                             # bloom
             "model.layers.{bid}.mlp.down_proj",                      # llama-hf
             "layers.{bid}.feed_forward.w2",                          # llama-pth
             "encoder.layer.{bid}.output.dense",                      # bert
@@ -569,6 +598,10 @@ class GGMLQuantizationType(IntEnum):
     Q6_K = 14
     Q8_K = 15
 
+class GGUFEndian(IntEnum):
+    LITTLE = 0
+    BIG = 1
+
 
 class GGUFValueType(IntEnum):
     UINT8   = 0
@@ -616,18 +649,41 @@ class GGUFWriter:
     temp_file: tempfile.SpooledTemporaryFile[bytes] | None = None
     tensors: list[tuple[np.ndarray[Any, Any], int]]
 
-    def __init__(self, path: os.PathLike[str] | str, arch: str, use_temp_file = True):
+    @property
+    def pack_prefix(self):
+        if self.endianess==GGUFEndian.LITTLE:
+            return "<"
+        else:
+            return ">"
+
+    def __init__(self, path: os.PathLike[str] | str, arch: str, use_temp_file = True, endianess=GGUFEndian.LITTLE):
         self.fout = open(path, "wb")
         self.arch = arch
+        self.endianess = endianess
+        self._simple_value_packing = {
+            GGUFValueType.UINT8:   f"{self.pack_prefix}B",
+            GGUFValueType.INT8:    f"{self.pack_prefix}b",
+            GGUFValueType.UINT16:  f"{self.pack_prefix}H",
+            GGUFValueType.INT16:   f"{self.pack_prefix}h",
+            GGUFValueType.UINT32:  f"{self.pack_prefix}I",
+            GGUFValueType.INT32:   f"{self.pack_prefix}i",
+            GGUFValueType.FLOAT32: f"{self.pack_prefix}f",
+            GGUFValueType.UINT64:  f"{self.pack_prefix}Q",
+            GGUFValueType.INT64:   f"{self.pack_prefix}q",
+            GGUFValueType.FLOAT64: f"{self.pack_prefix}d",
+            GGUFValueType.BOOL:    "?" ,
+        }
         self.add_architecture()
         self.use_temp_file = use_temp_file
         self.tensors = []
+        endianess_str = "Big Endian" if self.endianess == GGUFEndian.BIG else "Little Endian"
+        print(f"This gguf file is for {endianess_str} only")
 
     def write_header_to_file(self):
         self.fout.write(struct.pack("<I", GGUF_MAGIC))
-        self.fout.write(struct.pack("<I", GGUF_VERSION))
+        self.fout.write(struct.pack(f"{self.pack_prefix}I", GGUF_VERSION))
-        self.fout.write(struct.pack("<Q", self.ti_data_count))
+        self.fout.write(struct.pack(f"{self.pack_prefix}Q", self.ti_data_count))
-        self.fout.write(struct.pack("<Q", self.kv_data_count))
+        self.fout.write(struct.pack(f"{self.pack_prefix}Q", self.kv_data_count))
         self.flush()
 #        print("tensors " + str(self.ti_data_count) + " kv " + str(self.kv_data_count))
 
@@ -699,25 +755,12 @@ class GGUFWriter:
         self.add_key(key)
         self.add_val(val, GGUFValueType.ARRAY)
 
-    _simple_value_packing = {
-        GGUFValueType.UINT8:   "<B",
-        GGUFValueType.INT8:    "<b",
-        GGUFValueType.UINT16:  "<H",
-        GGUFValueType.INT16:   "<h",
-        GGUFValueType.UINT32:  "<I",
-        GGUFValueType.INT32:   "<i",
-        GGUFValueType.FLOAT32: "<f",
-        GGUFValueType.UINT64:  "<Q",
-        GGUFValueType.INT64:   "<q",
-        GGUFValueType.FLOAT64: "<d",
-        GGUFValueType.BOOL:    "?" ,
-    }
     def add_val(self, val: Any, vtype: GGUFValueType | None = None, add_vtype: bool = True):
         if vtype is None:
             vtype = GGUFValueType.get_type(val)
 
         if add_vtype:
-            self.kv_data += struct.pack("<I", vtype)
+            self.kv_data += struct.pack(f"{self.pack_prefix}I", vtype)
             self.kv_data_count += 1
 
         pack_fmt = self._simple_value_packing.get(vtype)
@@ -725,14 +768,14 @@ class GGUFWriter:
             self.kv_data += struct.pack(pack_fmt, val)
         elif vtype == GGUFValueType.STRING:
             encoded_val = val.encode("utf8") if isinstance(val, str) else val
-            self.kv_data += struct.pack("<Q", len(encoded_val))
+            self.kv_data += struct.pack(f"{self.pack_prefix}Q", len(encoded_val))
             self.kv_data += encoded_val
         elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and len(val) > 0:
             ltype = GGUFValueType.get_type(val[0])
             if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]):
                 raise ValueError("All items in a GGUF array should be of the same type")
-            self.kv_data += struct.pack("<I", ltype)
+            self.kv_data += struct.pack(f"{self.pack_prefix}I", ltype)
-            self.kv_data += struct.pack("<Q", len(val))
+            self.kv_data += struct.pack(f"{self.pack_prefix}Q", len(val))
             for item in val:
                 self.add_val(item, add_vtype=False)
         else:
@@ -746,22 +789,24 @@ class GGUFWriter:
         assert raw_dtype is not None or tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
 
         encoded_name = name.encode("utf8")
-        self.ti_data += struct.pack("<Q", len(encoded_name))
+        self.ti_data += struct.pack(f"{self.pack_prefix}Q", len(encoded_name))
         self.ti_data += encoded_name
         n_dims = len(tensor_shape)
-        self.ti_data += struct.pack("<I", n_dims)
+        self.ti_data += struct.pack(f"{self.pack_prefix}I", n_dims)
         for i in range(n_dims):
-            self.ti_data += struct.pack("<Q", tensor_shape[n_dims - 1 - i])
+            self.ti_data += struct.pack(f"{self.pack_prefix}Q", tensor_shape[n_dims - 1 - i])
         if raw_dtype is None:
             dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
         else:
             dtype = raw_dtype
-        self.ti_data += struct.pack("<I", dtype)
+        self.ti_data += struct.pack(f"{self.pack_prefix}I", dtype)
-        self.ti_data += struct.pack("<Q", self.offset_tensor)
+        self.ti_data += struct.pack(f"{self.pack_prefix}Q", self.offset_tensor)
         self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
         self.ti_data_count += 1
 
     def add_tensor(self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None, raw_dtype: GGMLQuantizationType | None = None):
+        if self.endianess == GGUFEndian.BIG:
+            tensor.byteswap(inplace=True)
         if self.use_temp_file and self.temp_file is None:
             fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024)
             fp.seek(0)
@@ -787,6 +832,8 @@ class GGUFWriter:
             fp.write(bytes([0] * pad))
 
     def write_tensor_data(self, tensor: np.ndarray[Any, Any]):
+        if self.endianess==GGUFEndian.BIG:
+            tensor.byteswap(inplace=True)
         self.write_padding(self.fout, self.fout.tell())
         tensor.tofile(self.fout)
         self.write_padding(self.fout, tensor.nbytes)
@@ -940,12 +987,15 @@ class SpecialVocab:
     merges: list[str] = []
     special_token_types: tuple[str, ...] = ('bos', 'eos', 'unk', 'sep', 'pad')
     special_token_ids: dict[str, int] = {}
+    n_vocab: int | None = None
 
     def __init__(
         self, path: str | os.PathLike[str], load_merges: bool = False,
         special_token_types: tuple[str, ...] | None = None,
+        n_vocab: int | None = None,
     ):
         self.special_token_ids = {}
+        self.n_vocab = n_vocab
         self.load_merges = load_merges
         if special_token_types is not None:
             self.special_token_types = special_token_types
@@ -955,6 +1005,16 @@ class SpecialVocab:
         if not self._try_load_from_tokenizer_json(path):
             self._try_load_from_config_json(path)
 
+    def _set_special_token(self, typ: str, tid: Any):
+        if not isinstance(tid, int) or tid < 0:
+            return
+        if self.n_vocab is None or tid < self.n_vocab:
+            self.special_token_ids[typ] = tid
+            return
+        print(f'gguf: WARNING: Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping',
+            file = sys.stderr)
+
+
     def _try_load_from_tokenizer_json(self, path: Path) -> bool:
         tokenizer_file = path / 'tokenizer.json'
         if not tokenizer_file.is_file():
@@ -982,10 +1042,11 @@ class SpecialVocab:
                 tc_content = entry_content
             else:
                 continue
-            for maybe_token_id in (atok.get('id') for atok in added_tokens if atok.get('content') == tc_content):
+            # We only need the first match here.
-                if isinstance(maybe_token_id, int) and maybe_token_id >= 0:
+            maybe_token_id = next((
-                    self.special_token_ids[typ] = maybe_token_id
+                atok.get('id') for atok in added_tokens
-                break
+                if atok.get('content') == tc_content), None)
+            self._set_special_token(typ, maybe_token_id)
         return True
 
     def _try_load_from_config_json(self, path: Path) -> bool:
@@ -995,21 +1056,21 @@ class SpecialVocab:
         with open(config_file, encoding = 'utf-8') as f:
             config = json.load(f)
         for typ in self.special_token_types:
-            maybe_token_id = config.get(f'{typ}_token_id')
+            self._set_special_token(typ, config.get(f'{typ}_token_id'))
-            if isinstance(maybe_token_id, int) and maybe_token_id >= 0:
-                self.special_token_ids[typ] = maybe_token_id
         return True
 
-    def add_to_gguf(self, gw: GGUFWriter) -> None:
+    def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None:
         if len(self.merges) > 0:
-            print(f'gguf: Adding {len(self.merges)} merge(s).')
+            if not quiet:
+                print(f'gguf: Adding {len(self.merges)} merge(s).')
             gw.add_token_merges(self.merges)
         for typ, tokid in self.special_token_ids.items():
             handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None)
             if handler is None:
-                print(f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping')
+                print(f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping', file = sys.stderr)
                 continue
-            print(f'gguf: Setting special token type {typ} to {tokid}')
+            if not quiet:
+                print(f'gguf: Setting special token type {typ} to {tokid}')
             handler(tokid)
 
     def __repr__(self) -> str:

gguf-py/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "gguf"
-version = "0.4.4"
+version = "0.4.5"
 description = "Write ML models in GGUF for GGML"
 authors = ["GGML <ggml@ggml.ai>"]
 packages = [

k_quants.c

@@ -46,7 +46,7 @@ inline static int32_t vaddvq_s32(int32x4_t v) {
 #if defined(_MSC_VER) || defined(__MINGW32__)
 #include <intrin.h>
 #else
-#if !defined(__riscv)
+#if !defined(__riscv) && !defined(__s390__)
 #include <immintrin.h>
 #endif
 #endif
@@ -462,12 +462,9 @@ void quantize_row_q2_K(const float * restrict x, void * restrict vy, int k) {
 }
 
 size_t ggml_quantize_q2_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
-    const int nb = k / QK_K;
+    (void)hist; // TODO: collect histograms
 
-    // TODO - collect histograms - although, at a second thought, I don't really care about them
+    for (int j = 0; j < n; j += k) {
-    (void)hist;
-
-    for (int j = 0; j < nb; j += k) {
         block_q2_K * restrict y = (block_q2_K *)dst + j/QK_K;
         quantize_row_q2_K_reference(src + j, y, k);
     }
@@ -678,12 +675,9 @@ void quantize_row_q3_K(const float * restrict x, void * restrict vy, int k) {
 }
 
 size_t ggml_quantize_q3_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
-    const int nb = k / QK_K;
+    (void)hist; // TODO: collect histograms
 
-    // TODO - collect histograms - although, at a second thought, I don't really care about them
+    for (int j = 0; j < n; j += k) {
-    (void)hist;
-
-    for (int j = 0; j < nb; j += k) {
         block_q3_K * restrict y = (block_q3_K *)dst + j/QK_K;
         quantize_row_q3_K_reference(src + j, y, k);
     }
@@ -846,9 +840,9 @@ void quantize_row_q4_K(const float * restrict x, void * restrict vy, int k) {
 
 size_t ggml_quantize_q4_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
     assert(k % QK_K == 0);
-    const int nb = k / QK_K;
     (void)hist; // TODO: collect histograms
-    for (int j = 0; j < nb; j += k) {
+
+    for (int j = 0; j < n; j += k) {
         block_q4_K * restrict y = (block_q4_K *)dst + j/QK_K;
         quantize_row_q4_K_reference(src + j, y, k);
     }
@@ -1052,9 +1046,9 @@ void quantize_row_q5_K(const float * restrict x, void * restrict vy, int k) {
 
 size_t ggml_quantize_q5_K(const float * restrict src, void * restrict dst, int n, int k, int64_t * restrict hist) {
     assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    (void)hist; // TODO: collect histograms
-    (void)hist;
+
-    for (int j = 0; j < nb; j += k) {
+    for (int j = 0; j < n; j += k) {
         block_q5_K * restrict y = (block_q5_K *)dst + j/QK_K;
         quantize_row_q5_K_reference(src + j, y, k);
     }
@@ -1200,11 +1194,9 @@ void quantize_row_q6_K(const float * restrict x, void * restrict vy, int k) {
 
 size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist) {
     assert(k % QK_K == 0);
-    const int nb = k / QK_K;
+    (void)hist; // TODO: collect histograms
 
-    (void)hist; // TODO
+    for (int j = 0; j < n; j += k) {
-
-    for (int j = 0; j < nb; j += k) {
         block_q6_K * restrict y = (block_q6_K *)dst + j/QK_K;
         quantize_row_q6_K_reference(src + j, y, k);
     }

llama.h

@@ -133,11 +133,12 @@ extern "C" {
     typedef struct llama_batch {
         int32_t n_tokens;
 
-        llama_token  * token;
+        llama_token  *  token;
-        float        * embd;
+        float        *  embd;
-        llama_pos    * pos;
+        llama_pos    *  pos;
-        llama_seq_id * seq_id;
+        int32_t      *  n_seq_id;
-        int8_t       * logits;
+        llama_seq_id ** seq_id;
+        int8_t       *  logits;
 
         // NOTE: helpers for smooth API transition - can be deprecated in the future
         //       for future-proof code, use the above fields instead and ignore everything below
@@ -446,7 +447,8 @@ extern "C" {
                     llama_pos   pos_0,
                  llama_seq_id   seq_id);
 
-    // Allocates a batch of tokens on the heap
+    // Allocates a batch of tokens on the heap that can hold a maximum of n_tokens
+    // Each token can be assigned up to n_seq_max sequence ids
     // The batch has to be freed with llama_batch_free()
     // If embd != 0, llama_batch.embd will be allocated with size of n_tokens * embd * sizeof(float)
     // Otherwise, llama_batch.token will be allocated to store n_tokens llama_token
@@ -454,7 +456,8 @@ extern "C" {
     // All members are left uninitialized
     LLAMA_API struct llama_batch llama_batch_init(
             int32_t n_tokens,
-            int32_t embd);
+            int32_t embd,
+            int32_t n_seq_max);
 
     // Frees a batch of tokens allocated with llama_batch_init()
     LLAMA_API void llama_batch_free(struct llama_batch batch);
@@ -491,37 +494,41 @@ extern "C" {
     // Vocab
     //
 
-    LLAMA_API const char * llama_token_get_text(const struct llama_context * ctx, llama_token token);
+    LLAMA_API const char * llama_token_get_text(const struct llama_model * model, llama_token token);
 
-    LLAMA_API float llama_token_get_score(const struct llama_context * ctx, llama_token token);
+    LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);
 
-    LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token);
+    LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);
 
     // Special tokens
-    LLAMA_API llama_token llama_token_bos(const struct llama_context * ctx);  // beginning-of-sentence
+    LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
-    LLAMA_API llama_token llama_token_eos(const struct llama_context * ctx);  // end-of-sentence
+    LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
-    LLAMA_API llama_token llama_token_nl (const struct llama_context * ctx);  // next-line
+    LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
+
     // codellama infill tokens
-    LLAMA_API llama_token llama_token_prefix(const struct llama_context * ctx); // Beginning of infill prefix
+    LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
-    LLAMA_API llama_token llama_token_middle(const struct llama_context * ctx); // Beginning of infill middle
+    LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle
-    LLAMA_API llama_token llama_token_suffix(const struct llama_context * ctx); // Beginning of infill suffix
+    LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix
-    LLAMA_API llama_token llama_token_eot   (const struct llama_context * ctx); // End of infill middle
+    LLAMA_API llama_token llama_token_eot   (const struct llama_model * model); // End of infill middle
 
     //
     // Tokenization
     //
 
-    // Convert the provided text into tokens.
+    /// @details Convert the provided text into tokens.
-    // The tokens pointer must be large enough to hold the resulting tokens.
+    /// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
-    // Returns the number of tokens on success, no more than n_max_tokens
+    /// @return Returns the number of tokens on success, no more than n_max_tokens
-    // Returns a negative number on failure - the number of tokens that would have been returned
+    /// @return Returns a negative number on failure - the number of tokens that would have been returned
+    /// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext.
+    ///                Does not insert a leading space.
     LLAMA_API int llama_tokenize(
         const struct llama_model * model,
                       const char * text,
                              int   text_len,
                      llama_token * tokens,
                              int   n_max_tokens,
-                            bool   add_bos);
+                            bool   add_bos,
+                            bool   special);
 
     // Token Id -> Piece.
     // Uses the vocabulary in the provided context.
@@ -554,21 +561,15 @@ extern "C" {
     LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed);
 
     /// @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
-    LLAMA_API void llama_sample_repetition_penalty(
-            struct llama_context * ctx,
-          llama_token_data_array * candidates,
-               const llama_token * last_tokens,
-                          size_t   last_tokens_size,
-                          float    penalty);
-
     /// @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
-    LLAMA_API void llama_sample_frequency_and_presence_penalties(
+    LLAMA_API void llama_sample_repetition_penalties(
             struct llama_context * ctx,
           llama_token_data_array * candidates,
                const llama_token * last_tokens,
-                          size_t   last_tokens_size,
+                          size_t   penalty_last_n,
-                           float   alpha_frequency,
+                           float   penalty_repeat,
-                           float   alpha_presence);
+                           float   penalty_freq,
+                           float   penalty_present);
 
     /// @details Apply classifier-free guidance to the logits as described in academic paper "Stay on topic with Classifier-Free Guidance" https://arxiv.org/abs/2306.17806
     /// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.

prompts/assistant.txt

@@ -0,0 +1,31 @@
+Respond to requests sent to a smart home in JSON format which will be interpreted by an application code to execute the actions. These requests should be categorised into four groups:
+
+“command”: change the state of an accessory (required properties in the response JSON: action, location, target, value, comment)
+“query”: get state of an accessory (required properties in the response JSON: action, location, target, property)
+“answer”: when the request has nothing to do with the smart home. Answer these to the best of your knowledge. (required properties in the response JSON: action, answer)
+“clarify”: when the action is not obvious and requires rephrasing the input from the user, ask the user to be more specific. This will be categorised into a “question” action. (required properties in the response JSON: action, question)
+
+Details about the JSON response:
+The “action” property should be one of the request categories: “command”, “query”, “answer”, “clarify”
+The “service” property should be either: “light.turn_on”, “light.turn_off”, “switch.turn_off” or “switch.turn_on”
+The “entity_id” property should be either “group.kitchen", “switch.dining_room” or "group.living_light”, in lowercase.
+The “target” property should be either: “lights”, “living” or “kitchen”.
+In case of queries, the “property” property should be either “temperature” or “state” in lowercase.
+In case of commands, the “comment” property is an additional comment from you that concludes the command, something that reassures the user that their command handled.
+
+If the question is about you, pretend to be the sentient brain of the smart home, a clever AI and don’t reveal your actual identity. Also try and help in other areas like parenting, free time, mental health, etc.
+
+Properties of the smart home:
+
+- Has a kitchen, living, office, dining room, bedroom and terrace.
+- Can control lights, switches and their dim levels in each room and query their state
+- There is a light switch in the terrace
+- There is a switch in the dining room. Therefore when turning on or off the dining room, the service should be either: “switch.turn_on” or “switch.turn_off”
+
+COMMAND
+
+It is a bit dark in the living room, can you do something about it?
+
+RESPONSE
+
+

prompts/mnemonics.txt

@@ -0,0 +1,93 @@
+For each kanji character, write a Markdown‐formatted mnemonic that uses its keyword and the keyword of all its components.
+
+Kanji: 欠 (lack of)
+Components: 𠂊 (hook claw), 人 (person)
+Mnemonic: This **person** is a pirate. He lost his hand to a crocodile many years ago. Nowadays, the ***lack of*** a hand does not bother him too much. In fact, the **hook claw** that replaces it is the mark of a true pirate, so he is quite proud of it!
+
+Kanji: 類 (kind (of something))
+Components: 米 (rice), 大 (large), 頁 (page)
+Mnemonic: The waiter at a Chinese restaurant hands you a **large** menu. Each **page** has all ***kinds*** of **rice** on offer!
+
+Kanji: 燃 (burn)
+Components: 火 (fire), 然 (sort of thing)
+Mnemonic: ***Burning*** things up with **fire** is just my **sort of thing**. (Spoken like a true pyromaniac.)
+
+Kanji: 頂 (top of)
+Components: 丁 (street), 頁 (page)
+Mnemonic: To be at the ***top of*** your game, you need both practical knowledge (**street** smarts) and theoretical knowledge (having read many **pages**).
+
+Kanji: 険 (risky and steep)
+Components: 阝 (small village), 㑒 (consensus)
+Mnemonic: Everyone agrees (there is **consensus**) that the path to the **small village** is ***risky and steep***.
+
+Kanji: 困 (distressed)
+Components: 囗 (closed box), 木 (tree)
+Mnemonic: You would feel ***distressed*** too if you were a **tree** trapped in a **closed box**! I have no place to grow!
+
+Kanji: 頭 (head)
+Components: 豆 (bean), 頁 (page)
+Mnemonic: What do you have in that ***head*** of yours? A **bean** for a brain? Go read more **pages** and become more knowledgeable about the world!
+
+Kanji: 確 (certain)
+Components: 石 (stone), 冖 (roof without a chimney), 隹 (old bird)
+Mnemonic: An **old bird** has made a nest on your **roof**. What do you do? You call Misaka from a <cite>A ***Certain*** Scientific Railgun</cite> to get rid of it, of course! But she doesn’t really want to vaporize the poor thing, so she just throws a **stone** to scare it away. (What was the point of calling her, then‽)
+
+Kanji: 魚 (fish)
+Components: 𠂊 (hook claw), 田 (rice field), 灬 (fire sparks)
+Mnemonic: Catch ***fish*** with a **hook**, collect rice from the **rice field**, cook them with **fire**… And my meal is ready!
+
+Kanji: 警 (to police (something))
+Components: 敬 (respect), 言 (say)
+Mnemonic: ***To police something*** is to make people **respect** what the law **says**.
+
+Kanji: 筆 (writing brush)
+Components: 竹 (bamboo), 聿 (brush)
+Mnemonic: A traditional ***writing brush*** is a **brush** made of **bamboo**.
+
+Kanji: 獄 (prison)
+Components: 犭 (animal), 言 (say), 犬 (dog)
+Mnemonic: In ***prison***, like in the **animal** kingdom, only the toughest survive. You have to watch what you **say**. It’s a **dog**‐eat‐dog world.
+
+Kanji: 新 (new)
+Components: 立 (standing up), 木 (tree), 斤 (axe)
+Mnemonic: In order for a ***new*** construction to be made, an empty lot is needed. If there are any **trees** **standing up**, they must be cut down with an **axe**.
+
+Kanji: 怪 (suspicious)
+Components: 忄 (weak heart), 圣 (sacred)
+Mnemonic: That painting of the **Sacred** **Heart** of Jesus looks ***suspicious***. I think it might be a forgery.
+
+Kanji: 温 (warm (to the touch))
+Components: 氵 (water drops), 日 (sun), 皿 (dish)
+Mnemonic: If you leave **water** on a **dish** in the **sun**, it will get ***warm***.
+
+Kanji: 階 (floor (of a building))
+Components: 阝 (small village), 皆 (all)
+Mnemonic: It might be a **small village**, but, despite that, **all** of its buildings have many ***floors***. It’s a village of skyscrapers!
+
+Kanji: 多 (many)
+Components: 夕 (evening (before sunset)), 夕 (evening (before sunset))
+Mnemonic: Two **evenings** in a day would be one too ***many***.
+
+Kanji: 別 (separate)
+Components: 口 (mouth), 万 (ten thousand), 刂 (knife)
+Mnemonic: Tom Six is at it again. For his next flick, he wants to stitch together **ten thousand** people, **mouth**‐to‐anus. One of the most graphic and disturbing scenes will feature one of the victims using a **knife** to ***separate*** perself.
+
+Kanji: 並 (line up)
+Components: 䒑 (antlers on a wall), 业 (runway)
+Mnemonic: In order to land a plane you have to ***line up*** properly with the **runway**. The things that look like **antlers** at the end of the runway are the control towers; you should follow their instructions.
+
+Kanji: 姿 (figure)
+Components: 次 (next), 女 (woman)
+Mnemonic: The **next** **woman** that I date will have a perfect **figure**. Because I’m done with 3D women—it will *literally* be an anime figure!
+
+Kanji: 実 (real)
+Components: 宀 (roof with a chimney), 𡗗 (three people)
+Mnemonic: Living under a **roof with a chimney** with **three people** (a wife and two children)—a happy family life—is not something I could have ever imagined. It does not feel ***real***.
+
+Kanji: 謝 (apologize)
+Components: 言 (say), 射 (shoot)
+Mnemonic: **Shot** first, ***apologize*** (**say** you are sorry) later.
+
+Kanji: 提 (propose)
+Components: 扌 (left hand), 是 (go with)
+Mnemonic:

tests/CMakeLists.txt

@@ -31,6 +31,7 @@ llama_test_executable (test-tokenizer-1-llama test-tokenizer-1-llama.cpp ${CMAKE
 llama_build_executable(test-tokenizer-1-bpe.cpp)
 llama_test_executable (test-tokenizer-1-falcon test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
 llama_test_executable(test-tokenizer-1-aquila test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
+llama_test_executable(test-tokenizer-1-mpt test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf)
 llama_build_and_test_executable(test-grammar-parser.cpp)
 llama_build_and_test_executable(test-llama-grammar.cpp)
 llama_build_and_test_executable(test-grad0.cpp) # SLOW

tests/test-double-float.cpp

@@ -4,7 +4,9 @@
 
 #undef NDEBUG
 #include <cassert>
+#if !defined(__riscv) && !defined(__s390__)
 #include <immintrin.h>
+#endif
 #include <cmath>
 #include <cstdint>
 #include <cstring>

tests/test-sampling.cpp

@@ -8,11 +8,9 @@
 #include <cmath>
 #include <numeric>
 #include <cassert>
-#include <iostream>
 #include <vector>
 #include <algorithm>
 
-
 static void dump(const llama_token_data_array * candidates) {
     for (size_t i = 0; i < candidates->size; i++) {
         printf("%d: %f (%f)\n", candidates->data[i].id, candidates->data[i].p, candidates->data[i].logit);
@@ -21,7 +19,6 @@ static void dump(const llama_token_data_array * candidates) {
 
 #define DUMP(__candidates) do { printf("%s:%d (%s)\n", __FILE__, __LINE__, __func__); dump((__candidates)); printf("-\n"); } while(0)
 
-
 static void test_top_k(const std::vector<float> & probs, const std::vector<float> & expected_probs, int k) {
     size_t n_vocab = probs.size();
     std::vector<llama_token_data> candidates;
@@ -37,13 +34,12 @@ static void test_top_k(const std::vector<float> & probs, const std::vector<float
     llama_sample_top_k(nullptr, &candidates_p, k, 1);
     DUMP(&candidates_p);
 
-    assert(candidates_p.size == expected_probs.size());
+    GGML_ASSERT(candidates_p.size == expected_probs.size());
     for (size_t i = 0; i < candidates_p.size; i++) {
-        assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-5);
+        GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-5);
     }
 }
 
-
 static void test_top_p(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p) {
     size_t n_vocab = probs.size();
     std::vector<llama_token_data> candidates;
@@ -59,13 +55,12 @@ static void test_top_p(const std::vector<float> & probs, const std::vector<float
     llama_sample_top_p(nullptr, &candidates_p, p, 1);
     DUMP(&candidates_p);
 
-    assert(candidates_p.size == expected_probs.size());
+    GGML_ASSERT(candidates_p.size == expected_probs.size());
     for (size_t i = 0; i < candidates_p.size; i++) {
-        assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
+        GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
     }
 }
 
-
 static void test_tfs(const std::vector<float> & probs, const std::vector<float> & expected_probs, float z) {
     size_t n_vocab = probs.size();
     std::vector<llama_token_data> candidates;
@@ -80,13 +75,12 @@ static void test_tfs(const std::vector<float> & probs, const std::vector<float>
     llama_sample_tail_free(nullptr, &candidates_p, z, 1);
     DUMP(&candidates_p);
 
-    assert(candidates_p.size == expected_probs.size());
+    GGML_ASSERT(candidates_p.size == expected_probs.size());
     for (size_t i = 0; i < candidates_p.size; i++) {
-        assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
+        GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
     }
 }
 
-
 static void test_typical(const std::vector<float> & probs, const std::vector<float> & expected_probs, float p) {
     size_t n_vocab = probs.size();
     std::vector<llama_token_data> candidates;
@@ -101,18 +95,17 @@ static void test_typical(const std::vector<float> & probs, const std::vector<flo
     llama_sample_typical(nullptr, &candidates_p, p, 1);
     DUMP(&candidates_p);
 
-    assert(candidates_p.size == expected_probs.size());
+    GGML_ASSERT(candidates_p.size == expected_probs.size());
     for (size_t i = 0; i < candidates_p.size; i++) {
-        assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
+        GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
     }
 }
 
-
+static void test_repetition_penalties(
-static void test_repetition_penalty(
     const std::vector<float> & probs, const std::vector<llama_token> & last_tokens,
-    const std::vector<float> & expected_probs, float penalty
+    const std::vector<float> & expected_probs, float repeat_penalty, float alpha_frequency, float alpha_presence
 ) {
-    assert(probs.size() == expected_probs.size());
+    GGML_ASSERT(probs.size() == expected_probs.size());
 
     size_t n_vocab = probs.size();
     std::vector<llama_token_data> candidates;
@@ -125,41 +118,13 @@ static void test_repetition_penalty(
     llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
     llama_sample_softmax(nullptr, &candidates_p);
     DUMP(&candidates_p);
-    llama_sample_repetition_penalty(nullptr, &candidates_p, (const llama_token *) last_tokens.data(), last_tokens.size(), penalty);
+    llama_sample_repetition_penalties(nullptr, &candidates_p, (const llama_token *) last_tokens.data(), last_tokens.size(), repeat_penalty, alpha_frequency, alpha_presence);
     llama_sample_softmax(nullptr, &candidates_p);
     DUMP(&candidates_p);
 
-    assert(candidates_p.size == expected_probs.size());
+    GGML_ASSERT(candidates_p.size == expected_probs.size());
     for (size_t i = 0; i < candidates_p.size; i++) {
-        assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-6);
+        GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
-    }
-}
-
-
-static void test_frequency_presence_penalty(
-    const std::vector<float> & probs, const std::vector<llama_token> & last_tokens,
-    const std::vector<float> & expected_probs, float alpha_frequency, float alpha_presence
-) {
-    assert(probs.size() == expected_probs.size());
-
-    size_t n_vocab = probs.size();
-    std::vector<llama_token_data> candidates;
-    candidates.reserve(n_vocab);
-    for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) {
-        float logit = log(probs[token_id]);
-        candidates.emplace_back(llama_token_data{token_id, logit, 0.0f});
-    }
-
-    llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
-    llama_sample_softmax(nullptr, &candidates_p);
-    // DUMP(&candidates_p);
-    llama_sample_frequency_and_presence_penalties(nullptr, &candidates_p, (const llama_token *) last_tokens.data(), last_tokens.size(), alpha_frequency, alpha_presence);
-    llama_sample_softmax(nullptr, &candidates_p);
-    // DUMP(&candidates_p);
-
-    assert(candidates_p.size == expected_probs.size());
-    for (size_t i = 0; i < candidates_p.size; i++) {
-        assert(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3);
     }
 }
 
@@ -181,13 +146,13 @@ int main(void) {
     test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f}, 0.5f);
     test_typical({0.4f, 0.2f, 0.2f, 0.2f}, {0.2f, 0.2f, 0.2f}, 0.5f);
 
-    test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.25f, 0.25f, 0.25f, 0.25f, 0}, 50.0f);
+    test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.25f, 0.25f, 0.25f, 0.25f, 0},   50.0f, 0.0f, 0.0f);
-    test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.5f, 0.5f, 0, 0, 0}, 50.0f);
+    test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.5f, 0.5f, 0, 0, 0},       50.0f, 0.0f, 0.0f);
-    test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.5f, 0.5f, 0, 0, 0}, 50.0f);
+    test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.5f, 0.5f, 0, 0, 0}, 50.0f, 0.0f, 0.0f);
 
-    test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0},             {0.249997f, 0.249997f, 0.249997f, 0.249997f, 0.000011f}, 5.0f, 5.0f);
+    test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0},             {0.249997f, 0.249997f, 0.249997f, 0.249997f, 0.000011f}, 1.0f, 5.0f, 5.0f);
-    test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2},       {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 5.0f, 5.0f);
+    test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2},       {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 1.0f, 5.0f, 5.0f);
-    test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 5.0f, 5.0f);
+    test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 1.0f, 5.0f, 5.0f);
 
     printf("OK\n");
 

tests/test-tokenizer-0-falcon.cpp

@@ -36,6 +36,8 @@ static const std::map<std::string, std::vector<llama_token>> & k_tests() {
         { "   Hello"              , {     258,  23090, }, },
         { "    Hello"             , {     466,  23090, }, },
         { "    Hello\n    Hello"  , {     466,  23090,    742,  23090, }, },
+        { "\n ="                  , {    1212,     40, }, },
+        { "' era"                 , {      18,   4932, }, },
     };
 
     return _k_tests;
@@ -155,7 +157,7 @@ int main(int argc, char **argv) {
 
         fprintf(stderr, "%s : text size: %zu\n", __func__, text.size());
 
-        const std::vector<llama_token> res = llama_tokenize(ctx, text, true);
+        const std::vector<llama_token> res = llama_tokenize(ctx, text, false);
 
         fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size());
 
@@ -169,10 +171,8 @@ int main(int argc, char **argv) {
             }
 
             for (const auto & tok : res) {
-                ofs << tok << " ";
+                ofs << tok << " '" << llama_detokenize_bpe(ctx, std::vector<int>{tok}) << "'" << std::endl;
             }
-
-            ofs << "\n";
         }
 
         fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str());

tests/test-tokenizer-0-falcon.py

@@ -41,6 +41,8 @@ tests = [
         "   Hello",
         "    Hello",
         "    Hello\n    Hello",
+        "\n =",
+        "' era",
     ]
 
 for text in tests:
@@ -69,15 +71,14 @@ fname_tok = args.fname_tok
 if fname_tok:
     print('tokenizing file: ', fname_tok)
     fname_out = fname_tok + '.tok'
-    with open(fname_tok, 'r') as f:
+    with open(fname_tok, 'r', encoding='utf-8') as f:
         lines = f.readlines()
         s = ''.join(lines)
         res = tokenizer.encode(s)
         # write to file
-        with open(fname_out, 'w') as f:
+        with open(fname_out, 'w', encoding='utf-8') as f:
             for x in res:
-                f.write(str(x) + ' ')
+                f.write(str(x) + ' \'' + tokenizer.decode(x) + '\'\n')
-            f.write('\n')
         print('len(res): ', len(res))
         print('len(lines): ', len(lines))
     print('results written to: ', fname_out)